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Updated: 2 hours 27 min ago

What’s the status of WebRTC in 2019?

Mon, 06/17/2019 - 12:00

In 2019, WebRTC is ready, but there’s still work ahead.

When I wrote that WebRTC is ready over 6 months ago it pissed a few people off.

Here’s the thing – WebRTC is ready simply because the industry deems it ready and companies are deploying products that rely on WebRTC to work for them.

Are there challenges along the way? Sure.

Do things break? Sure.

But if you are thinking of whether you should start using WebRTC and build an application on top of it or wait for the next fad to come by for your video calling service, then don’t. Use WebRTC as nothing else will do today.

Trying to understand where WebRTC is available? Download my free cheat sheet

WebRTC device cheat sheet

WebRTC 1.0 – the specification

In 2015 I remember someone telling me that WebRTC 1.0 will be closed and published by year end.

I heard the same in 2016. And later in 2017.

In 2018 I ignored such promises.

2019? There is a small chance that things will be ready. Why? Because the spec is almost completed. That almost is the sticking point.

But then again, who cares?

Everyone is already using WebRTC as if it is a done deal. Because it is.

We’ve agreed on the technology (WebRTC). We’ve agreed on the larger picture and the ways things are going to look like (peer connection and how browsers implement it today). We’re left with the nitty gritty details of how to make the experience easier and uniform across browsers for developers. We will get there, but just remember – users expect it to work, and it does.

Chrome and WebRTC

Consider Chrome to be the de facto specification for WebRTC. It isn’t WebRTC 1.0 compliant. Yet. According to Statista, 69% of the desktop internet is driven by Chrome. On this website? 74% of the viewers use Chrome.

The thing about Chrome is that it is slowly getting the missing WebRTC 1.0 support, and by moving there it is breaking things up with each release. Usually because the way it works today isn’t exactly spec compliant, so things have to break – or just because the additions are delicate and the work done breaks behavior that developers relied on in the past. At times, it is because Google has no qualms when it comes to technical debt and code rewrites and when it sees a need to optimize something it usually does that (we’re now in the 3rd generation of echo canceller in WebRTC, each one was a complete rewrite of the previous one).

If you are developing anything that needs to run in the browser and use WebRTC, then Chrome is the first thing you should be developing for.

Firefox and WebRTC

Firefox is close to be spec compliant when it comes to WebRTC.

They had it easy with the recent decision to adopt Unified Plan instead of Plan B in the WebRTC specification. Where Google had to shift from Plan B to Unified Plan, Firefox had only slight modifications to make.

The problem is that Firefox is a distant second to Chrome in market share. At times, developers actively decide not to support Firefox just because they consider it a waste of time. This is doubly true for those who use Chrome for guest access and as a stepping stone to getting their users to download their Electron app instead.

Safari and WebRTC

Safari now supports WebRTC. That includes things like simulcast and both VP8 and H.264. Which is to say that most WebRTC features already work in Safari, but not all of them.

You wouldn’t find VP9 which isn’t mandatory or popular yet, but something that is more than desirable. And then some of the more complicated scenarios such as multiparty sessions have more pending open issues of both functionality and interoperability than Chrome or Firefox have.

The challenge is that Safari is important to developers. Both because it is the only way to get on iOS devices and because it is the default browser for Mac, a desktop/laptop that for some reason is becoming a fad with developers (go figure).

Edge and WebRTC

Edge was once its own browser with its own technology stack, but is now becoming just another flavor of Chrome. Microsoft announced that Edge will be using Chromium as its browser engine. This has gotten Edge to work on Mac already with rumors of a possible Linux release.

Edge runs on Chromium.

Chrome runs on Chromium.

Chrome isn’t WebRTC spec compliant because Chromium isn’t WebRTC spec compliant.

So Edge isn’t spec compliant either. But it is well… the same as Chrome.

This all relates to the upcoming official release of Edge.

Microsoft IE and WebRTC

Still dream about Internet Explorer at night?

Stop it.

IE won’t be supporting WebRTC. Not now and not ever.

Use a plugin or just use Electron. Or better yet – update to a more modern browser.

Opera/Brave/whoever and WebRTC

Most of the other browsers out there, be it Opera, Brave or anything else is just a fork of Chromium or a skin on top of Chromium.

For all intent and purpose, they are Chrome, offering the same spec compliance to WebRTC as Chrome does. At least if they haven’t gone and intentionally made changes to it (like disabling it in the name of privacy).

Android and WebRTC

Android has support of WebRTC.

Chrome browser that ships with Android has WebRTC support.

Other browsers shipping on Android have WebRTC support (such as Firefox).

Sometimes, a device manufacturer ends up shipping his own browser (Samsung for example). Then WebRTC compliance and availability is somewhat questionable.

The good thing is that the Webview in Android also supports WebRTC. So built-in application browsers such as the one used by Facebook or Slack also end up supporting WebRTC experiences.

And if you write your own app, you can use the Webview, a precompiled version of WebRTC for Android or compile it on your own.

iOS and WebRTC

On iOS things are slightly trickier.

Safari supports WebRTC on iOS and there are companies making commercial use of it already.

Other browsers don’t and can’t support WebRTC on iOS. That’s because the supplied iOS Webview still doesn’t support WebRTC (or disables it on purpose).

If you write your own app, you can use a precompiled version of WebRTC for iOS or compile it on your own. No Webview for you yet.

Your Next Steps?

Haven’t started with WebRTC yet? Now’s the time. I can help.

Trying to understand where WebRTC is available? Download my free cheat sheet

WebRTC device cheat sheet

The post What’s the status of WebRTC in 2019? appeared first on BlogGeek.me.

WebRTC video recording may be more useful than WebRTC video calling

Mon, 06/03/2019 - 12:00

Video recording using WebRTC can be a lot more lucrative a business than WebRTC video calling.

There’s been an ongoing rumble around WebRTC in a lot of discussions I had about it and sometimes from what you read online – What’s the market size of WebRTC? How do you make money out of it? Who is making money out of it?

Questions that are really hard to answer. Usually because people don’t like to hear the answers to them.

Looking to understand where and how to fit WebRTC into your business? Let’s talk

Contact Tsahi

The Zoom IPO

Is there money in video conferencing or video calling?

The service today is practically free, spread across a multitude of different service types:

Social
  • Apple FaceTime
  • Google Duo & Google Hangouts
  • Facebook Messenger
  • WhatsApp
  • Skype
  • Houseparty

An unending list of social communication services that happen to have video calling in them. I’ve bunched Apple and Google in here simply because they “own” the smartphones we use today.

Business
  • Google Meet
  • Zoom

Here you’ll find services that are free to a certain extent. They are either time limited, feature limited, or just bundled up to bigger offerings.

Zoom were probably the first to go this route with a well-featured product where the biggest limit for a free account was time – 40 minutes per session. Long enough for a lot of uses.

Consumer/Soho

There are many consumer-type services that got built using WebRTC and gained traction. The services started as free offerings, and each grew of its own accord. Jitsi Meet got acquired by Atlassian and then 8×8 acquired it from Atlassian. Appear.in started offering paid Pro accounts and got acquired by Videonor. Talky became a showcase for SimpleWebRTC.

Others started with a free service, ending with a paid service, like Gruveo.

Show me the money

This is where things got complicated.

No one saw a way to make money out of WebRTC. Or video.

At least not until Zoom IPO’d. ~$425 million annual run rate, growing at over 100% a year. Alex Clayton has a nice breakdown of their filing:

The moment this happened, both BlueJeans and LifeSize decided to publish their numbers – BlueJeans reached $100m ARR while Lifesize reached $100m in bookings. Their message? Zoom isn’t alone.

For the record, and to make this clear:

  • Zoom doesn’t use WebRTC
  • BlueJeans and Lifesize use WebRTC though both existed before WebRTC

The thing here is video conferencing service, and how do you make money out of it? You can, if you’re big enough, though it will be hard to join the game now and try to outdo Zoom in video conferencing by using their playbook.

The challenge is probably that everyone is looking under the light post.

You’ve got practically 100s of developers, startups, enterprises and whatnots vying towards disrupting the video conferencing market with WebRTC. The challenge is that with so many players coming in with the same technology, only a few will stay standing.

Differentiation is tough in this space. Why would someone pick up your service and not another? How will they find you? Why should they pay?

Which brings me to the reason I started writing this in the first place –

Not video calling – WebRTC video recording

I went to AppSumo this week, deciding to purchase another deal on their site. Every once in awhile I find there some great deals and new services to use for my business. The latest featured offer on that site? Dubb (now sold out)

Dubb

This is a service that runs as a Chrome extension enabling its users to record a short video and share it with customers over SMS, email or other networks.

I don’t know if Dubb supports WebRTC or not, but –

  1. It works in the browser with no need to install anything (besides a Chrome extension)
  2. It records video and voice right there inside the browser

In all likelihood, this is using WebRTC’s MediaRecorder to record locally and upload the result to the Dubb cloud service.

Dubb is positioned as a sales tool to build rapport – not as a video conferencing or a communication tool. There’s no “real time”, “collaboration” or “conferencing” here.

Seeing it got me thinking of another tool I bumped into recently – Loom

Loom

I started a coaching program a few months back. My WebRTC Course showed success in the last 3 years of its existence and I wanted to grow it in size – have more people enroll and learn WebRTC in the process. The coaching program is interesting. I am learning a ton in it, some of it already found its way into the course and a lot more will be coming in the next course launch in a few months time.

Anyways, when I ask questions via email, I usually get back video recordings of my coach reviewing the question and answering it, thinking through the issues I raise. I can see him and his screen, which is great. The link and tool he uses? Loom.

So I checked it out:

Similarly to Dubb, this one is about recording videos from the browser, with no installation needed. In Loom’s case, they are even trying to showcase the various uses of their tool.

WebRTC isn’t only about calling

WebRTC isn’t only about calling.

It has other capabilities. There’s the data channel, there’s the simple access to the camera and mic and there’s the ability to record media on the client side to name a few.

That client side recording enables these services – Dubb and Loom. there’s also Ziggeo and Pipe for those looking for a managed API for it.

I am wondering. When everyone is closely looking at video calling, trying to figure out how to make $$$ out of that space, is the real usability of WebRTC lies elsewhere altogether?

Looking to understand where and how to fit WebRTC into your business? Let’s talk

Contact Tsahi

The post WebRTC video recording may be more useful than WebRTC video calling appeared first on BlogGeek.me.

WebRTC video recording may be more useful than WebRTC video calling

Mon, 06/03/2019 - 12:00

Video recording using WebRTC can be a lot more lucrative a business than WebRTC video calling.

There’s been an ongoing rumble around WebRTC in a lot of discussions I had about it and sometimes from what you read online – What’s the market size of WebRTC? How do you make money out of it? Who is making money out of it?

Questions that are really hard to answer. Usually because people don’t like to hear the answers to them.

Looking to understand where and how to fit WebRTC into your business? Let’s talk

Contact Tsahi

The Zoom IPO

Is there money in video conferencing or video calling?

The service today is practically free, spread across a multitude of different service types:

Social
  • Apple FaceTime
  • Google Duo & Google Hangouts
  • Facebook Messenger
  • WhatsApp
  • Skype
  • Houseparty

An unending list of social communication services that happen to have video calling in them. I’ve bunched Apple and Google in here simply because they “own” the smartphones we use today.

Business
  • Google Meet
  • Zoom

Here you’ll find services that are free to a certain extent. They are either time limited, feature limited, or just bundled up to bigger offerings.

Zoom were probably the first to go this route with a well-featured product where the biggest limit for a free account was time – 40 minutes per session. Long enough for a lot of uses.

Consumer/Soho

There are many consumer-type services that got built using WebRTC and gained traction. The services started as free offerings, and each grew of its own accord. Jitsi Meet got acquired by Atlassian and then 8×8 acquired it from Atlassian. Appear.in started offering paid Pro accounts and got acquired by Videonor. Talky became a showcase for SimpleWebRTC.

Others started with a free service, ending with a paid service, like Gruveo.

Show me the money

This is where things got complicated.

No one saw a way to make money out of WebRTC. Or video.

At least not until Zoom IPO’d. ~$425 million annual run rate, growing at over 100% a year. Alex Clayton has a nice breakdown of their filing:

The moment this happened, both BlueJeans and LifeSize decided to publish their numbers – BlueJeans reached $100m ARR while Lifesize reached $100m in bookings. Their message? Zoom isn’t alone.

For the record, and to make this clear:

  • Zoom doesn’t use WebRTC
  • BlueJeans and Lifesize use WebRTC though both existed before WebRTC

The thing here is video conferencing service, and how do you make money out of it? You can, if you’re big enough, though it will be hard to join the game now and try to outdo Zoom in video conferencing by using their playbook.

The challenge is probably that everyone is looking under the light post.

You’ve got practically 100s of developers, startups, enterprises and whatnots vying towards disrupting the video conferencing market with WebRTC. The challenge is that with so many players coming in with the same technology, only a few will stay standing.

Differentiation is tough in this space. Why would someone pick up your service and not another? How will they find you? Why should they pay?

Which brings me to the reason I started writing this in the first place –

Not video calling – WebRTC video recording

I went to AppSumo this week, deciding to purchase another deal on their site. Every once in awhile I find there some great deals and new services to use for my business. The latest featured offer on that site? Dubb (now sold out)

Dubb

This is a service that runs as a Chrome extension enabling its users to record a short video and share it with customers over SMS, email or other networks.

I don’t know if Dubb supports WebRTC or not, but –

  1. It works in the browser with no need to install anything (besides a Chrome extension)
  2. It records video and voice right there inside the browser

In all likelihood, this is using WebRTC’s MediaRecorder to record locally and upload the result to the Dubb cloud service.

Dubb is positioned as a sales tool to build rapport – not as a video conferencing or a communication tool. There’s no “real time”, “collaboration” or “conferencing” here.

Seeing it got me thinking of another tool I bumped into recently – Loom

Loom

I started a coaching program a few months back. My WebRTC Course showed success in the last 3 years of its existence and I wanted to grow it in size – have more people enroll and learn WebRTC in the process. The coaching program is interesting. I am learning a ton in it, some of it already found its way into the course and a lot more will be coming in the next course launch in a few months time.

Anyways, when I ask questions via email, I usually get back video recordings of my coach reviewing the question and answering it, thinking through the issues I raise. I can see him and his screen, which is great. The link and tool he uses? Loom.

So I checked it out:

Similarly to Dubb, this one is about recording videos from the browser, with no installation needed. I Loom’s case, they are even trying to showcase the various uses of their tool.

WebRTC isn’t only about calling

WebRTC isn’t only about calling.

It has other capabilities. There’s the data channel, there’s the simple access to the camera and mic and there’s the ability to record media on the client side to name a few.

That client side recording enables these services – Dubb and Loom. there’s also Ziggeo and Pipe for those looking for a managed API for it.

I am wondering. When everyone is closely looking at video calling, trying to figure out how to make $$$ out of that space, is the real usability of WebRTC lies elsewhere altogether?

Looking to understand where and how to fit WebRTC into your business? Let’s talk

Contact Tsahi

The post WebRTC video recording may be more useful than WebRTC video calling appeared first on BlogGeek.me.

WebRTC vs WebSockets

Tue, 05/28/2019 - 12:00

WebRTC vs WebSockets: They. Are. Not. The. Same.

Sometimes, there are things that seem obvious once you’re “in the know” but just isn’t that when you’re new to the topic. It seems that the difference between WebRTC vs WebSockets is one such thing. Philipp Hancke pinged me the other day, asking if I have an article about WebRTC vs WebSockets, and I didn’t – it made no sense for me. That at least, until I asked Google about it:

It seems like Google believes the most pressing (and popular) search for comparisons of WebRTC is between WebRTC and WebSockets. I should probably also write about them other comparisons there, but for now, let’s focus on that first one.

Need to learn WebRTC? Check out my online course – the first module is free.

Learn WebRTC

What are WebSockets?

WebSockets are a bidirectional mechanism for browser communication.

There are two types of transport channels for communication in browsers: HTTP and WebSockets.

HTTP is what gets used to fetch web pages, images, stylesheets and javascript files as well as other resources. In essence, HTTP is a client-server protocol, where the browser is the client and the web server is the server:

My WebRTC course covers this in detail, but suffice to say here that with HTTP, your browser connects to a web server and requests *something* of it. The server then sends a response to that request and that’s the end of it.

The challenge starts when you want to send an unsolicited message from the server to the client. You can’t do it if you don’t send a request from the web browser to the web server, and while you can use different schemes such as XHR and SSE to do that, they end up feeling like hacks or workarounds more than solutions.

Enter WebSockets, what’s meant to solve exactly that – the web browser connects to the web server by establishing a WebSocket connection. Over that connection, both the browser and the server can send each other unsolicited messages. Not only that, they can send binary (gasp!) messages – something impossible without yet another hack (known as base64) in HTTP.

Because WebSockets are built-for-purpose and not the alternative XHR/SSE hacks, WebSockets perform better both in terms of speed and resources it eats up on both browsers and servers.

WebSockets are rather simple to use as a web developer – you’ve got a straightforward WebSocket API for them, which are nicely illustrated by HPBN:

var ws = new WebSocket('wss://example.com/socket'); ws.onerror = function (error) { ... } ws.onclose = function () { ... } ws.onopen = function () { ws.send("Connection established. Hello server!"); } ws.onmessage = function(msg) { if(msg.data instanceof Blob) { processBlob(msg.data); } else { processText(msg.data); } }

You’ve got calls for send and close and callbacks for onopen, onerror, onclose and onmessage. Of course there’s more to it than that, but this is holds the essence of WebSockets.

It leads us to what we usually use WebSockets for, and I’d like to explain it this time not by actual scenarios and use cases but rather by the keywords I’ve seen associated with WebSockets:

  • Bi-directional, full-duplex
  • Signaling
  • Real-time data transfer
  • Low latency
  • Interactive
  • High performance
  • Chat, two way conversation

Funnily, a lot of this sometimes get associated with WebRTC as well, which might be the cause of the comparison that is made between the two.

WebRTC, in the context of WebSockets

There are numerous articles here about WebRTC, including a What is WebRTC one.

In the context of WebRTC vs WebSockets, WebRTC enables sending arbitrary data across browsers without the need to relay that data through a server (most of the time). That data can be voice, video or just data.

Here’s where things get interesting –

WebRTC has no signaling channel

When starting a WebRTC session, you need to negotiate the capabilities for the session and the connection itself. That is done out of the scope of WebRTC, in whatever means you deem fit. And in a browser, this can either be HTTP or… WebSocket.

So from this point of view, WebSocket isn’t a replacement to WebRTC but rather complementary – as an enabler.

You can send media over a WebSocket

Sort of.

I’ll start with an example. If you want you connect to a cloud based speech to text API and you happen to use IBM Watson, then you can use its WebSocket interface. The first sentence in the first paragraph of the documentation?

The WebSocket interface of the Speech to Text service is the most natural way for a client to interact with the service.

So. you stream the speech (=voice) over a WebSocket to connect it to the cloud API service.

That said, it is highly unlikely to be used for anything else.

In most cases, real time media will get sent over WebRTC or other protocols such as RTSP, RTMP, HLS, etc.

WebRTC’s data channel

WebRTC has a data channel. It has many different uses. In some cases, it is used in place of using a kind of a WebSocket connection:

The illustration above shows how a message would pass from one browser to another over a WebSocket versus doing the same over a WebRTC data channel. Each has its advantages and challenges.

Funnily, the data channel in WebRTC shares a similar set of APIs to the WebSocket ones:

const peerConnection = new RTCPeerConnection(); const dataChannel = peerConnection.createDataChannel("myLabel", dataChannelOptions); dataChannel.onerror = (error) => { … }; dataChannel.onclose = () => { … }; dataChannel.onopen = () => { dataChannel.send("Hello World!"); }; dataChannel.onmessage = (event) => { … };

Again, we’ve got calls for send and close and callbacks for onopen, onerror, onclose and onmessage.

This makes an awful lot of sense but can be confusing a bit.

There this one tiny detail – to get the data channel working, you first need to negotiate the connection. And that you do either with HTTP or with a WebSocket.

When should you use WebRTC instead of a WebSocket?

Almost never. That’s the truth.

If you’re contemplating between the two and you don’t know a lot about WebRTC, then you’re probably in need of WebSockets, or will be better off using WebSockets.

I’d think of data channels either when there are things you want to pass directly across browsers without any server intervention in the message itself (and these use cases are quite scarce), or you are in need of a low latency messaging solution across browsers where a relay via a WebSocket will be too time consuming.

Need to learn WebRTC? Check out my online course – the first module is free.

Learn WebRTC

The post WebRTC vs WebSockets appeared first on BlogGeek.me.

WebRTC vs WebSockets

Tue, 05/28/2019 - 12:00

WebRTC vs WebSockets: They. Are. Not. The. Same.

Sometimes, there are things that seem obvious once you’re “in the know” but just isn’t that when you’re new to the topic. It seems that the difference between WebRTC vs WebSockets is one such thing. Philipp Hancke pinged me the other day, asking if I have an article about WebRTC vs WebSockets, and I didn’t – it made no sense for me. That at least, until I asked Google about it:

It seems like Google believes the most pressing (and popular) search for comparisons of WebRTC is between WebRTC and WebSockets. I should probably also write about them other comparisons there, but for now, let’s focus on that first one.

Need to learn WebRTC? Check out my online course – the first module is free.

Learn WebRTC

What are WebSockets?

WebSockets are a bidirectional mechanism for browser communication.

There are two types of transport channels for communication in browsers: HTTP and WebSockets.

HTTP is what gets used to fetch web pages, images, stylesheets and javascript files as well as other resources. In essence, HTTP is a client-server protocol, where the browser is the client and the web server is the server:

My WebRTC course covers this in detail, but suffice to say here that with HTTP, your browser connects to a web server and requests *something* of it. The server then sends a response to that request and that’s the end of it.

The challenge starts when you want to send an unsolicited message from the server to the client. You can’t do it if you don’t send a request from the web browser to the web server, and while you can use different schemes such as XHR and SSE to do that, they end up feeling like hacks or workarounds more than solutions.

Enter WebSockets, what’s meant to solve exactly that – the web browser connects to the web server by establishing a WebSocket connection. Over that connection, both the browser and the server can send each other unsolicited messages. Not only that, they can send binary (gasp!) messages – something impossible without yet another hack (known as base64) in HTTP.

Because WebSockets are built-for-purpose and not the alternative XHR/SSE hacks, WebSockets perform better both in terms of speed and resources it eats up on both browsers and servers.

WebSockets are rather simple to use as a web developer – you’ve got a straightforward WebSocket API for them, which are nicely illustrated by HPBN:

var ws = new WebSocket('wss://example.com/socket'); ws.onerror = function (error) { ... } ws.onclose = function () { ... } ws.onopen = function () { ws.send("Connection established. Hello server!"); } ws.onmessage = function(msg) { if(msg.data instanceof Blob) { processBlob(msg.data); } else { processText(msg.data); } }

You’ve got calls for send and close and callbacks for onopen, onerror, onclose and onmessage. Of course there’s more to it than that, but this is holds the essence of WebSockets.

It leads us to what we usually use WebSockets for, and I’d like to explain it this time not by actual scenarios and use cases but rather by the keywords I’ve seen associated with WebSockets:

  • Bi-directional, full-duplex
  • Signaling
  • Real-time data transfer
  • Low latency
  • Interactive
  • High performance
  • Chat, two way conversation

Funnily, a lot of this sometimes get associated with WebRTC as well, which might be the cause of the comparison that is made between the two.

WebRTC, in the context of WebSockets

There are numerous articles here about WebRTC, including a What is WebRTC one.

In the context of WebRTC vs WebSockets, WebRTC enables sending arbitrary data across browsers without the need to relay that data through a server (most of the time). That data can be voice, video or just data.

Here’s where things get interesting –

WebRTC has no signaling channel

When starting a WebRTC session, you need to negotiate the capabilities for the session and the connection itself. That is done out of the scope of WebRTC, in whatever means you deem fit. And in a browser, this can either be HTTP or… WebSocket.

So from this point of view, WebSocket isn’t a replacement to WebRTC but rather complementary – as an enabler.

You can send media over a WebSocket

Sort of.

I’ll start with an example. If you want you connect to a cloud based speech to text API and you happen to use IBM Watson, then you can use its WebSocket interface. The first sentence in the first paragraph of the documentation?

The WebSocket interface of the Speech to Text service is the most natural way for a client to interact with the service.

So. you stream the speech (=voice) over a WebSocket to connect it to the cloud API service.

That said, it is highly unlikely to be used for anything else.

In most cases, real time media will get sent over WebRTC or other protocols such as RTSP, RTMP, HLS, etc.

WebRTC’s data channel

WebRTC has a data channel. It has many different uses. In some cases, it is used in place of using a kind of a WebSocket connection:

The illustration above shows how a message would pass from one browser to another over a WebSocket versus doing the same over a WebRTC data channel. Each has its advantages and challenges.

Funnily, the data channel in WebRTC shares a similar set of APIs to the WebSocket ones:

const peerConnection = new RTCPeerConnection(); const dataChannel = peerConnection.createDataChannel("myLabel", dataChannelOptions); dataChannel.onerror = (error) => { … }; dataChannel.onclose = () => { … }; dataChannel.onopen = () => { dataChannel.send("Hello World!"); }; dataChannel.onmessage = (event) => { … };

Again, we’ve got calls for send and close and callbacks for onopen, onerror, onclose and onmessage.

This makes an awful lot of sense but can be confusing a bit.

There this one tiny detail – to get the data channel working, you first need to negotiate the connection. And that you do either with HTTP or with a WebSocket.

When should you use WebRTC instead of a WebSocket?

Almost never. That’s the truth.

If you’re contemplating between the two and you don’t know a lot about WebRTC, then you’re probably in need of WebSockets, or will be better off using WebSockets.

I’d think of data channels either when there are things you want to pass directly across browsers without any server intervention in the message itself (and these use cases are quite scarce), or you are in need of a low latency messaging solution across browsers where a relay via a WebSocket will be too time consuming.

Need to learn WebRTC? Check out my online course – the first module is free.

Learn WebRTC

The post WebRTC vs WebSockets appeared first on BlogGeek.me.

WebRTC simulcast and ABR – two sides of the same coin

Mon, 05/20/2019 - 12:00

WebRTC simulcast and ABR is all about offer choice to “viewers”.

I’ve been dealing recently with more clients who are looking to create live broadcast experiences. Solutions where one or more users have to broadcast their streams from a single session to a large audience. Large is a somewhat lenient target number, which seems to be stretching from anywhere between 100 to a 1,000,000 viewers. And yes, most of these clients want that viewers will have instantaneous access to the stream(s) – a lag of 1-2 seconds at most, as opposed to the 10 or more seconds of latency you get from HLS.

Simulcast, ABR – need a quick reference to understand their similarities and differences? Download the free cheatsheet:

Compare simulcast to ABR

What I started seeing more and more recently are solutions that make use of ABR. What’s ABR? It is just like simulcast, but… different.

What’s Simulcast?

Simulcast is a mechanism in WebRTC by which a device/client/user will be sending a video stream that contains multiple bitrates in it. I explained it a bit in my WebRTC Multiparty Architectures last month.

With simlucast, a WebRTC client will generate these multiple bitrates, where each offers a different video quality – the higher the bitrate the higher the quality.

These video streams are then received by the SFU, and the SFU can pick and choose which stream to send to which participant/viewer. This decision is usually made based on the available bandwidth, but it can (and should) make use of a lot of other factors as well – display size and video layout on the viewer device, CPU utilization of the viewer, etc.

The great thing about simulcast? The SFU doesn’t work too hard. It just selects what to send where.

What’s ABR?

ABR stands for Adaptive Bitrate Streaming. Don’t ask me why R and not S in the acronym – probably because they didn’t want to mix this with car breaks. Anyways, ABR comes from streaming, long before WebRTC was introduced to our lives.

With streaming, you’ve got a user watching a recorded (or “live”) video online. The server then streams that media towards the user. What happens if the available bitrate from the server to the user is low? Buffering.

Streaming technology uses TCP, which in turn uses retransmissions. It isn’t designed for real-time, and well… we want to SEE the content and would rather wait a bit than not see it at all.

Today, with 1080p and 4K resolutions, streaming at high quality requires lots and lots of bandwidth. If the network isn’t capable, would users rather wait and be buffered or would it be better to just lower the quality?

Most prefer lowering the quality.

But how do you do that with “static” content? A pre-recorded video file is what it is.

You use ABR:

With ABR, you segment bandwidth into ranges. Each range will be receiving a different media stream. Each such stream has a different bitrate.

Say you have a media stream of 300kbps – you define the segment bandwidth for it as 300-500kbps. Why? Because from 500kbps there’s another media stream available.

These media streams all contain the same content, just in different bitrates, denoting different quality levels. What you try doing is sending the highest quality range to each viewer without getting into that dreaded buffering state. Since the available bitrate is dynamic in nature (as the illustration above shows), you can end up switching across media streams based on the bitrate available to the viewer at any given point in time. That’s why they call it adaptive.

And it sounds rather similar to simulcast… just on the server side, as ABR is something a server generates – the original media gets to a server, which creates multiple output streams to it in different bitrates, to use when needed.

The ABR challenge for WebRTC media servers

Recently, I’ve seen more discussions and solutions looking at using ABR and similar techniques with WebRTC. Mainly to scale a session beyond 10k viewers and to support low latency broadcasting in CDNs.

Why these two areas?

  1. Because beyond 10k viewers, simulcast isn’t enough anymore. Simulcast today supports up to 3 media streams and the variety you get with 10k viewers is higher than that. There are a few other reasons as well, but that’s for another time
  2. Because CDNs and video streaming have been comfortable with ABR for years now, so them shifting towards WebRTC or low latency means they are looking for much the same technologies and mechanisms they already know

But here’s the problem.

We’ve been doing SFUs with WebRTC for most of the time that WebRTC existed. Around 7-8 years. We’re all quite comfortable now with the concept of paying on bandwidth and not eating too much CPU – which is the performance profile of an SFU.

Simulcast fits right into that philosophy – the one creating the alternate streams is the client and not the SFU – it is sending more media towards the SFU who now has more options. The client pays the price of higher bitrates and higher CPU use.

ABR places that burden on the server, which needs to generate the additional alternate streams on its own, and it needs to do so in real time – there’s no offline pre-processing activity for generating these streams from a pre-existing media file as there is with CDNs. this means that SFUs now need to think about CPU loads, muck around with transcoding, experiment with GPU acceleration – the works. Things they haven’t done so far.

Is this in our future? Sure it is. For some, it is already their present.

Simulcast, ABR – need a quick reference to understand their similarities and differences? Download the free cheatsheet:

Compare simulcast to ABR

What’s next?

WebRTC is growing and evolving. The ecosystem around it is becoming much richer as time goes by. Today, you can find different media servers of different types and characteristics, and the solutions available are quite different from one another.

If you are planning on developing your own application using a media server – make sure you pick a media server that fits to your use case.

The post WebRTC simulcast and ABR – two sides of the same coin appeared first on BlogGeek.me.

WebRTC simulcast and ABR – two sides of the same coin

Mon, 05/20/2019 - 12:00

WebRTC simulcast and ABR is all about offer choice to “viewers”.

I’ve been dealing recently with more clients who are looking to create live broadcast experiences. Solutions where one or more users have to broadcast their streams from a single session to a large audience. Large is a somewhat lenient target number, which seems to be stretching from anywhere between 100 to a 1,000,000 viewers. And yes, most of these clients want that viewers will have instantaneous access to the stream(s) – a lag of 1-2 seconds at most, as opposed to the 10 or more seconds of latency you get from HLS.

Simulcast, ABR – need a quick reference to understand their similarities and differences? Download the free cheatsheet:

Compare simulcast to ABR

What I started seeing more and more recently are solutions that make use of ABR. What’s ABR? It is just like simulcast, but… different.

What’s Simulcast?

Simulcast is a mechanism in WebRTC by which a device/client/user will be sending a video stream that contains multiple bitrates in it. I explained it a bit in my WebRTC Multiparty Architectures last month.

With simlucast, a WebRTC client will generate these multiple bitrates, where each offers a different video quality – the higher the bitrate the higher the quality.

These video streams are then received by the SFU, and the SFU can pick and choose which stream to send to which participant/viewer. This decision is usually made based on the available bandwidth, but it can (and should) make use of a lot of other factors as well – display size and video layout on the viewer device, CPU utilization of the viewer, etc.

The great thing about simulcast? The SFU doesn’t work too hard. It just selects what to send where.

What’s ABR?

ABR stands for Adaptive Bitrate Streaming. Don’t ask me why R and not S in the acronym – probably because they didn’t want to mix this with car breaks. Anyways, ABR comes from streaming, long before WebRTC was introduced to our lives.

With streaming, you’ve got a user watching a recorded (or “live”) video online. The server then streams that media towards the user. What happens if the available bitrate from the server to the user is low? Buffering.

Streaming technology uses TCP, which in turn uses retransmissions. It isn’t designed for real-time, and well… we want to SEE the content and would rather wait a bit than not see it at all.

Today, with 1080p and 4K resolutions, streaming at high quality requires lots and lots of bandwidth. If the network isn’t capable, would users rather wait and be buffered or would it be better to just lower the quality?

Most prefer lowering the quality.

But how do you do that with “static” content? A pre-recorded video file is what it is.

You use ABR:

With ABR, you segment bandwidth into ranges. Each range will be receiving a different media stream. Each such stream has a different bitrate.

Say you have a media stream of 300kbps – you define the segment bandwidth for it as 300-500kbps. Why? Because from 500kbps there’s another media stream available.

These media streams all contain the same content, just in different bitrates, denoting different quality levels. What you try doing is sending the highest quality range to each viewer without getting into that dreaded buffering state. Since the available bitrate is dynamic in nature (as the illustration above shows), you can end up switching across media streams based on the bitrate available to the viewer at any given point in time. That’s why they call it adaptive.

And it sounds rather similar to simulcast… just on the server side, as ABR is something a server generates – the original media gets to a server, which creates multiple output streams to it in different bitrates, to use when needed.

The ABR challenge for WebRTC media servers

Recently, I’ve seen more discussions and solutions looking at using ABR and similar techniques with WebRTC. Mainly to scale a session beyond 10k viewers and to support low latency broadcasting in CDNs.

Why these two areas?

  1. Because beyond 10k viewers, simulcast isn’t enough anymore. Simulcast today supports up to 3 media streams and the variety you get with 10k viewers is higher than that. There are a few other reasons as well, but that’s for another time
  2. Because CDNs and video streaming have been comfortable with ABR for years now, so them shifting towards WebRTC or low latency means they are looking for much the same technologies and mechanisms they already know

But here’s the problem.

We’ve been doing SFUs with WebRTC for most of the time that WebRTC existed. Around 7-8 years. We’re all quite comfortable now with the concept of paying on bandwidth and not eating too much CPU – which is the performance profile of an SFU.

Simulcast fits right into that philosophy – the one creating the alternate streams is the client and not the SFU – it is sending more media towards the SFU who now has more options. The client pays the price of higher bitrates and higher CPU use.

ABR places that burden on the server, which needs to generate the additional alternate streams on its own, and it needs to do so in real time – there’s no offline pre-processing activity for generating these streams from a pre-existing media file as there is with CDNs. this means that SFUs now need to think about CPU loads, muck around with transcoding, experiment with GPU acceleration – the works. Things they haven’t done so far.

Is this in our future? Sure it is. For some, it is already their present.

Simulcast, ABR – need a quick reference to understand their similarities and differences? Download the free cheatsheet:

Compare simulcast to ABR

What’s next?

WebRTC is growing and evolving. The ecosystem around it is becoming much richer as time goes by. Today, you can find different media servers of different types and characteristics, and the solutions available are quite different from one another.

If you are planning on developing your own application using a media server – make sure you pick a media server that fits to your use case.

The post WebRTC simulcast and ABR – two sides of the same coin appeared first on BlogGeek.me.

Google I/O 2019 was all about AI, Privacy and Accessibility

Mon, 05/13/2019 - 12:00

At Google I/O 2019, the advances Google made in AI and machine learning were put to use for improving privacy and accessibility.

I’ve attended Google I/O in person only once. It was in 2014. I’ve been following this event from afar ever since, making it a point to watch the keynote each year, trying to figure out where Google is headed – and how will that affect the industry.

This weekend I spend some time going over te Google I/O 2019 keynote. If you haven’t seen it, you can watch it over on YouTube – I’ve embedded it here as well.

The main theme of Google I/O 2019

Here’s how I ended my review about Google I/O 2018:

Where are we headed?

That’s the big question I guess.

More machine learning and AI. Expect Google I/O 2019 to be on the same theme.

If you don’t have it in your roadmap, time to see how to fit it in.

In many ways, this can easily be the end of this article as well – the tl;dr version.

Google got to the heart of their keynote only in around the 36 minute mark. Sundar Pichai, CEO of Google, talked about the “For Everyone” theme of this event and where Google is headed. For Everyone – not only for the rich (Apple?) or the people in developed countries, but For Everyone.

The first thing he talked about in this For Everyone context? AI:

From there, everything Google does is about how the AI research work and breakthroughs that they are doing at their scale can fit into the direction they want to take.

This year, that direction was defined by the words privacy, security and accessibility.

Privacy because they are being scrutinized over their data collection, which is directly linked to their business model. But more so because of a recent breakthrough that enables them to run accurate speech to text on devices (more on that later).

Security because of the growing number of hacking and malware attacks we hear about all the time. But more so because the work Google has put into Android from all aspects is placing them ahead on competition (think Apple) based on third party reports (Gartner in this case).

Interestingly, Apple is attacking Google around both privacy and security.

Accessibility because that’s the next billion users. The bigger market. The way to grow by reaching ever larger audiences. But also because it fits well with that breakthrough in speech to text and with machine learning as a whole. And somewhat because of diversity and inclusion which are big words and concepts in tech and silicon valley these days (and you need to appease the crowds and your own employees). And also because it films well and it really does benefit the world and people – though that’s secondary for companies.

The big reveal for me at Google I/O 2019? Definitely its advances in speech analytics by getting speech to text minimized enough to fit into a mobile device. It was the main pillar of this show and for things to come in the future if you ask me.

A lot of the AI innovations Google is talking about is around real time communications. Check out the recent report I’ve written with Chad Hart on the subject:

AI in RTC report

Event Timeline

I wanted to understand what is important to Google this year, so I took a rough timeline of the event, breaking it down into the minutes spent on each topic. In each and every topic discussed, machine learning and AI were apparent.

Time spentTopic10 minSearch; introduction of new feature(s)8 minGoogle Lens; introduction of new feature(s) – related to speech to text16 minGoogle assistant (Duplex on the web, assistant, driving mode)19 minFor Everyone (AI, bias, privacy+security, accessibility)14 minAndroid Q enhancements and innovations (software)9 minNext (home)9 minPixel (smartphone hardware)16 minGoogle AI

Let’s put this in perspective: out of roughly 100 minutes, 51 were spent directly on AI (assistant, for everyone and AI) and the rest of the time was spent about… AI, though indirectly.

Watching the event, I must say it got me thinking of my time at the university. I had a neighbor at the dorms who was a professional juggler. Maybe not professional, but he did get paid for juggling from time to time. He was able to juggle 5 torches or clubs, 5 apples (while eating one) and anywhere between 7-11 balls (I didn’t keep track).

One evening he comes storming into our room, asking us all to watch a new trick he was working on and just perfected. We all looked. And found it boring. Not because it wasn’t hard or impressive, but because we all knew that this was most definitely within his comfort zone and the things he can do. Funny thing is – he visited us here in Israel a few weeks back. My wife asked him if he juggles anymore. He said a bit, and said his kids aren’t impressed. How could they when it is obvious to them that he can?

Anyways, there’s no wow factor in what Google is doing with machine learning anymore. It is obvious that each year, in every Google I/O event, some new innovation around this topic will be introduced.

This time, it was all about voice and text.

Time to dive into what went on @ Google I/O 2019 keynote.

Speech to text on device

We had a glimpse of this piece of technology late last year when Google introduced call screening to its Pixel 3 devices. This capability allows people to let the Pixel answer calls on their behalf, see what people are saying using live transcription and decide how to act.

This was all done on device. At Google I/O 2019, this technology was just added across the board on Android 10 to anything and everything.

On stage, the explanation given was that the model used for speech to text in the cloud is 2.5Gb in size, and Google was able to squeeze it down to 80Mb, which meant being able to run it on devices. It was not indicated if this is for any language other than English, which probably meant this is an English only capability for now.

What does Google gain from this capability?

  1. Faster speech to text. There’s no need to send audio to the cloud and get text back from it
  2. Ability to run it with no network or with poor network conditions
  3. Privacy of what’s being said

For now, Google will be rolling this out to Android devices and not just Google Pixel devices. No mention of if or when this gets to iOS devices.

What have they done with it?

  • Made the Google assistant more responsive (due to faster speech to text)
  • Created system-wide automatic captioning for everything that runs on Android. Anywhere, on any app
Search

The origins of Google came from Search, and Google decided to start the keynote with search.

Nothing super interesting there in the announcements made, besides the continuous improvements. What was showcased was news and podcasts.

How Google decided to handle Face News and news coverage is now coming to search directly. Podcasts are now made searchable and better accessible directly from search.

Other than that?

A new shiny object – the ability to show 3D models in search results and in augmented reality.

Nice, but not earth shattering. At least not yet.

Google Lens

After Search, Google Lens was showcased.

The main theme around it? The ability to capture text in real time on images and do stuff with it. Usually either text to speech or translation.

In the screenshot above, Google Lens marks the recommended dishes off a menu. While nice, this probably requires each and every such feature to be baked into lens, much like new actions need to be baked into the Google Assistant (or skills in Amazon Alexa).

This falls nicely into the For Everyone / Accessibility theme of the keynote. Aparna Chennapragada, Head of Product for Lens, had the following to say (after an emotional video of a woman who can’t read using the new Lens):

“The power to read is the power to buy a train ticket. To shop in a store. To follow the news. It is the power to get things done. So we want to make this feature to be as accessible to as many people as possible, so it already works in a dozen of languages.”

It actually is. People can’t really be part of our world without the power to read.

It is also the only announcement I remember that the number of languages covered was mentioned (which is why I believe speech to text on device is English only).

Google made the case here and in almost every part of the keynote in favor of using AI for the greater good – for accessibility and inclusion.

Google assistant

Google assistant had its share of the keynote with 4 main announcements:

Duplex on the web is a smarter auto fill feature for web forms.

Next generation Assistant is faster and smarter than its predecessor. There were two main aspects of it that were really interesting to me:

  1. It is “10 times faster”, most probably due to speech to text on the phone which doesn’t necessitate the cloud for many tasks
  2. It works across tabs and apps. A demo was shown, where a the woman instructed the Assistant to search for a photo, picking one out and then asking the phone to send it on an ongoing chat conversation just by saying “send it to Justin”

Every year Google seems to be making Assistant more conversational, able to handle more intents and actions – and understand a lot more of the context necessary for complex tasks.

For Everyone

I’ve written about For Everyone earlier in this article.

I want to cover two more aspect of it, federated learning and project euphonia.

Federated Learning

Machine learning requires tons of data. The more data the better the resulting model is at predicting new inputs. Google is often criticized for collecting that data, but it needs it not only for monetization but also a lot for improving its AI models.

Enter federated learning, a way to learn a bit at the edge of the network, directly inside the devices, and share what gets learned in a secure fashion with the central model that is being created in the cloud.

This was so important for Google to show and explain that Sundar Pichai himself showed and gave that spiel instead of leaving it to the final part of the keynote where Google AI was discussed almost separately.

At Google, this feels like an initiative that is only starting its way with the first public implementation of it embedded as part of Google’s predictive keyboard on Android and how that keyboard is learning new words and trends.

Project Euphonia

Project Euphonia was also introduced here. This project is about enhancing speech recognition models towards hard to understand speech.

Here Google stressed the work and effort it is putting on collecting recorded phrases from people with such problems. The main issue here being the creation or improvement of a model more than anything else.

Android Q

Or Android 10 – pick your name for it.

This one was more than anything else a shopping list of features.

Statistics were given at the beginning:

  • 2.5 billion active devices
  • Over 180 device makers

Live captions was again explained and introduced, along with on-device learning capabilities. AI at its best baked into the OS itself.

For some reason, the Android Q segment wasn’t followed with the Pixel one but rather with the Nest one.

Nest (helpful home)

Google rebranded all of its smart home devices under Nest.

While at it, the decided to try and differentiate from the rest of the pack by coining their solution the “helpful home” as opposed to the “smart home”.

As with everything else, AI and the assistant took center stage, as well as a new device, the Nest Hub Max, which is Google’s answer to the Facebook Portal.

The solution for video calling on the Next Hub Max was built around Google Duo (obviously), with a similar ability to auto zoom that Facebook Portal has, at least on paper – it wasn’t really demoed or showcased on stage.

The reason no demo was really given is that this device will ship “later this summer”, which means it wasn’t really ready for prime time – or Google just didn’t want to spend more precious minutes on it during the keynote.

Interestingly, Google Duo’s recent addition of group video calling wasn’t mentioned throughout the keynote at all.

Pixel (phone)

The Pixel section of the keynote showcased a new Pixel phone device, the Pixel 3a and 3a XL. This is a low cost device, which tries to make do with lower hardware spec by offering better software and AI capabilities. To drive that point home, Google had this slide to show:

Google is continuing with its investment in computational photography, and if the results are as good as this example, I am sold.

The other nice feature shown was call screening:

The neet thing is that your phone can act as your personal secretary, checking for you who’s calling and why, and also converse with the caller based on your instructions. This obviously makes use of the same innovations in Android around speech to text and smart reply.

My current phone is Xiaomi Mi A1, an Android One device. My next one may well be the Pixel 3a – at $399, it will probably be the best phone on the market at that price point.

Google AI

The last section of the keynote was given by Jeff Dean, head of Google.ai. He was also the one closing the keynote, instead of handing this back to Sundar Pichai. I found that nuance interesting.

In his part he discussed the advancements in natural language understanding (NLU) at Google, the growth of TensorFlow, where Google is putting its efforts in healthcare (this time it was oncology and lung cancer), as well as the AI for Social Good initiative, where flood forecasting was explained.

That finishing touch of Google AI in the keynote, taking 16 full minutes (about 15% of the time) shows that Google was aiming to impress and to focus on the good they are making in the world, trying to reduce the growing fear factor of their power and data collection capabilities.

It was impressive…

Next year?

More of the same is my guess.

Google will need to find some new innovation to build their event around. Speech to text on device is great, especially with the many use cases it enabled and the privacy angle to it. Not sure how they’d top that next year.

What’s certain is that AI and privacy will still be at the forefront for Google during 2019 and well into 2020.

A lot of the AI innovations Google is talking about is around real time communications. Check out the recent report I’ve written with Chad Hart on the subject:

AI in RTC report

The post Google I/O 2019 was all about AI, Privacy and Accessibility appeared first on BlogGeek.me.

Google I/O 2019 was all about AI, Privacy and Accessibility

Mon, 05/13/2019 - 12:00

At Google I/O 2019, the advances Google made in AI and machine learning were put to use for improving privacy and accessibility.

I’ve attended Google I/O in person only once. It was in 2014. I’ve been following this event from afar ever since, making it a point to watch the keynote each year, trying to figure out where Google is headed – and how will that affect the industry.

This weekend I spend some time going over te Google I/O 2019 keynote. If you haven’t seen it, you can watch it over on YouTube – I’ve embedded it here as well.

The main theme of Google I/O 2019

Here’s how I ended my review about Google I/O 2018:

Where are we headed?

That’s the big question I guess.

More machine learning and AI. Expect Google I/O 2019 to be on the same theme.

If you don’t have it in your roadmap, time to see how to fit it in.

In many ways, this can easily be the end of this article as well – the tl;dr version.

Google got to the heart of their keynote only in around the 36 minute mark. Sundar Pichai, CEO of Google, talked about the “For Everyone” theme of this event and where Google is headed. For Everyone – not only for the rich (Apple?) or the people in developed countries, but For Everyone.

The first thing he talked about in this For Everyone context? AI:

From there, everything Google does is about how the AI research work and breakthroughs that they are doing at their scale can fit into the direction they want to take.

This year, that direction was defined by the words privacy, security and accessibility.

Privacy because they are being scrutinized over their data collection, which is directly linked to their business model. But more so because of a recent breakthrough that enables them to run accurate speech to text on devices (more on that later).

Security because of the growing number of hacking and malware attacks we hear about all the time. But more so because the work Google has put into Android from all aspects is placing them ahead on competition (think Apple) based on third party reports (Gartner in this case).

Interestingly, Apple is attacking Google around both privacy and security.

Accessibility because that’s the next billion users. The bigger market. The way to grow by reaching ever larger audiences. But also because it fits well with that breakthrough in speech to text and with machine learning as a whole. And somewhat because of diversity and inclusion which are big words and concepts in tech and silicon valley these days (and you need to appease the crowds and your own employees). And also because it films well and it really does benefit the world and people – though that’s secondary for companies.

The big reveal for me at Google I/O 2019? Definitely its advances in speech analytics by getting speech to text minimized enough to fit into a mobile device. It was the main pillar of this show and for things to come in the future if you ask me.

A lot of the AI innovations Google is talking about is around real time communications. Check out the recent report I’ve written with Chad Hart on the subject:

AI in RTC report

Event Timeline

I wanted to understand what is important to Google this year, so I took a rough timeline of the event, breaking it down into the minutes spent on each topic. In each and every topic discussed, machine learning and AI were apparent.

Time spentTopic10 minSearch; introduction of new feature(s)8 minGoogle Lens; introduction of new feature(s) – related to speech to text16 minGoogle assistant (Duplex on the web, assistant, driving mode)19 minFor Everyone (AI, bias, privacy+security, accessibility)14 minAndroid Q enhancements and innovations (software)9 minNext (home)9 minPixel (smartphone hardware)16 minGoogle AI

Let’s put this in perspective: out of roughly 100 minutes, 51 were spent directly on AI (assistant, for everyone and AI) and the rest of the time was spent about… AI, though indirectly.

Watching the event, I must say it got me thinking of my time at the university. I had a neighbor at the dorms who was a professional juggler. Maybe not professional, but he did get paid for juggling from time to time. He was able to juggle 5 torches or clubs, 5 apples (while eating one) and anywhere between 7-11 balls (I didn’t keep track).

One evening he comes storming into our room, asking us all to watch a new trick he was working on and just perfected. We all looked. And found it boring. Not because it wasn’t hard or impressive, but because we all knew that this was most definitely within his comfort zone and the things he can do. Funny thing is – he visited us here in Israel a few weeks back. My wife asked him if he juggles anymore. He said a bit, and said his kids aren’t impressed. How could they when it is obvious to them that he can?

Anyways, there’s no wow factor in what Google is doing with machine learning anymore. It is obvious that each year, in every Google I/O event, some new innovation around this topic will be introduced.

This time, it was all about voice and text.

Time to dive into what went on @ Google I/O 2019 keynote.

Speech to text on device

We had a glimpse of this piece of technology late last year when Google introduced call screening to its Pixel 3 devices. This capability allows people to let the Pixel answer calls on their behalf, see what people are saying using live transcription and decide how to act.

This was all done on device. At Google I/O 2019, this technology was just added across the board on Android 10 to anything and everything.

On stage, the explanation given was that the model used for speech to text in the cloud is 2.5Gb in size, and Google was able to squeeze it down to 80Mb, which meant being able to run it on devices. It was not indicated if this is for any language other than English, which probably meant this is an English only capability for now.

What does Google gain from this capability?

  1. Faster speech to text. There’s no need to send audio to the cloud and get text back from it
  2. Ability to run it with no network or with poor network conditions
  3. Privacy of what’s being said

For now, Google will be rolling this out to Android devices and not just Google Pixel devices. No mention of if or when this gets to iOS devices.

What have they done with it?

  • Made the Google assistant more responsive (due to faster speech to text)
  • Created system-wide automatic captioning for everything that runs on Android. Anywhere, on any app
Search

The origins of Google came from Search, and Google decided to start the keynote with search.

Nothing super interesting there in the announcements made, besides the continuous improvements. What was showcased was news and podcasts.

How Google decided to handle Face News and news coverage is now coming to search directly. Podcasts are now made searchable and better accessible directly from search.

Other than that?

A new shiny object – the ability to show 3D models in search results and in augmented reality.

Nice, but not earth shattering. At least not yet.

Google Lens

After Search, Google Lens was showcased.

The main theme around it? The ability to capture text in real time on images and do stuff with it. Usually either text to speech or translation.

In the screenshot above, Google Lens marks the recommended dishes off a menu. While nice, this probably requires each and every such feature to be baked into lens, much like new actions need to be baked into the Google Assistant (or skills in Amazon Alexa).

This falls nicely into the For Everyone / Accessibility theme of the keynote. Aparna Chennapragada, Head of Product for Lens, had the following to say (after an emotional video of a woman who can’t read using the new Lens):

“The power to read is the power to buy a train ticket. To shop in a store. To follow the news. It is the power to get things done. So we want to make this feature to be as accessible to as many people as possible, so it already works in a dozen of languages.”

It actually is. People can’t really be part of our world without the power to read.

It is also the only announcement I remember that the number of languages covered was mentioned (which is why I believe speech to text on device is English only).

Google made the case here and in almost every part of the keynote in favor of using AI for the greater good – for accessibility and inclusion.

Google assistant

Google assistant had its share of the keynote with 4 main announcements:

Duplex on the web is a smarter auto fill feature for web forms.

Next generation Assistant is faster and smarter than its predecessor. There were two main aspects of it that were really interesting to me:

  1. It is “10 times faster”, most probably due to speech to text on the phone which doesn’t necessitate the cloud for many tasks
  2. It works across tabs and apps. A demo was shown, where a the woman instructed the Assistant to search for a photo, picking one out and then asking the phone to send it on an ongoing chat conversation just by saying “send it to Justin”

Every year Google seems to be making Assistant more conversational, able to handle more intents and actions – and understand a lot more of the context necessary for complex tasks.

For Everyone

I’ve written about For Everyone earlier in this article.

I want to cover two more aspect of it, federated learning and project euphonia.

Federated Learning

Machine learning requires tons of data. The more data the better the resulting model is at predicting new inputs. Google is often criticized for collecting that data, but it needs it not only for monetization but also a lot for improving its AI models.

Enter federated learning, a way to learn a bit at the edge of the network, directly inside the devices, and share what gets learned in a secure fashion with the central model that is being created in the cloud.

This was so important for Google to show and explain that Sundar Pichai himself showed and gave that spiel instead of leaving it to the final part of the keynote where Google AI was discussed almost separately.

At Google, this feels like an initiative that is only starting its way with the first public implementation of it embedded as part of Google’s predictive keyboard on Android and how that keyboard is learning new words and trends.

Project Euphonia

Project Euphonia was also introduced here. This project is about enhancing speech recognition models towards hard to understand speech.

Here Google stressed the work and effort it is putting on collecting recorded phrases from people with such problems. The main issue here being the creation or improvement of a model more than anything else.

Android Q

Or Android 10 – pick your name for it.

This one was more than anything else a shopping list of features.

Statistics were given at the beginning:

  • 2.5 billion active devices
  • Over 180 device makers

Live captions was again explained and introduced, along with on-device learning capabilities. AI at its best baked into the OS itself.

For some reason, the Android Q segment wasn’t followed with the Pixel one but rather with the Nest one.

Nest (helpful home)

Google rebranded all of its smart home devices under Nest.

While at it, the decided to try and differentiate from the rest of the pack by coining their solution the “helpful home” as opposed to the “smart home”.

As with everything else, AI and the assistant took center stage, as well as a new device, the Nest Hub Max, which is Google’s answer to the Facebook Portal.

The solution for video calling on the Next Hub Max was built around Google Duo (obviously), with a similar ability to auto zoom that Facebook Portal has, at least on paper – it wasn’t really demoed or showcased on stage.

The reason no demo was really given is that this device will ship “later this summer”, which means it wasn’t really ready for prime time – or Google just didn’t want to spend more precious minutes on it during the keynote.

Interestingly, Google Duo’s recent addition of group video calling wasn’t mentioned throughout the keynote at all.

Pixel (phone)

The Pixel section of the keynote showcased a new Pixel phone device, the Pixel 3a and 3a XL. This is a low cost device, which tries to make do with lower hardware spec by offering better software and AI capabilities. To drive that point home, Google had this slide to show:

Google is continuing with its investment in computational photography, and if the results are as good as this example, I am sold.

The other nice feature shown was call screening:

The neet thing is that your phone can act as your personal secretary, checking for you who’s calling and why, and also converse with the caller based on your instructions. This obviously makes use of the same innovations in Android around speech to text and smart reply.

My current phone is Xiaomi Mi A1, an Android One device. My next one may well be the Pixel 3a – at $399, it will probably be the best phone on the market at that price point.

Google AI

The last section of the keynote was given by Jeff Dean, head of Google.ai. He was also the one closing the keynote, instead of handing this back to Sundar Pichai. I found that nuance interesting.

In his part he discussed the advancements in natural language understanding (NLU) at Google, the growth of TensorFlow, where Google is putting its efforts in healthcare (this time it was oncology and lung cancer), as well as the AI for Social Good initiative, where flood forecasting was explained.

That finishing touch of Google AI in the keynote, taking 16 full minutes (about 15% of the time) shows that Google was aiming to impress and to focus on the good they are making in the world, trying to reduce the growing fear factor of their power and data collection capabilities.

It was impressive…

Next year?

More of the same is my guess.

Google will need to find some new innovation to build their event around. Speech to text on device is great, especially with the many use cases it enabled and the privacy angle to it. Not sure how they’d top that next year.

What’s certain is that AI and privacy will still be at the forefront for Google during 2019 and well into 2020.

A lot of the AI innovations Google is talking about is around real time communications. Check out the recent report I’ve written with Chad Hart on the subject:

AI in RTC report

The post Google I/O 2019 was all about AI, Privacy and Accessibility appeared first on BlogGeek.me.

Google CallJoy & the age of automation in communications

Mon, 05/06/2019 - 12:00

ML/AI is coming to communications really fast. It is going to manifest is as automation in communications but also in other ways.

Me? I wanted to talk about automation and communications. But then Google released CallJoy, which was… automation and communications. And it shows where we’re headed quite clearly with a service that is butt simple, and yet… Google seems to be the first at it, at least when it comes to aiming for simplicity and a powerful MVP. Here’s where I took this article –

Ever since Google launched Duplex at I/O 2018 I’ve been wondering what’s next. Google came out with a new service called CallJoy – a kind of a voice assistant/agent for small businesses. Before I go into the age of automation and communications, let’s try to find out where machine learning and artificial intelligence can be found in CallJoy.

Interested in AI in communications? Tomorrow I’ll be hosting a webinar with Chad Hart on this topic – join us:

Register to the webinar

CallJoy and AI

What CallJoy does exactly?

From the CallJoy website, it looks that the following takes place: you subscribe for the service, pick a local phone number to use and you’re good to go.

When people call your business, they get greeted by a message (“this call is being recorded for whatever purposes” kind of a thing). Next, it can “share” information such as business hours and ask if the caller wants to do stuff over a web link instead of talking to a human. If a web link is what you want (think a “yes please” answer to whatever you hear on the phone when you call), then you’ll get an SMS with a URL. Otherwise, you’ll just get routed to the business’ “real” phone number to be handled by a human. All calls get recorded.

What machine learning aspects does this service use?

#1 – Block unwanted spam calls

Incoming spam calls can really harass small businesses. Being able to get less of these is always a blessing. It is also becoming a big issue in the US, one that brings a lot of attention and some attempts at solving it by carriers as well as other vendors.

I am not sure what blocking does Google do here and if it makes direct use of machine learning or not – it certainly can. The fact that all calls get handled by a chatbot means that there’s some kind of a “gating” process that a spam call needs to pass first. This in itself blocks at least some of them spam calls.

#2 – Call deflection, using a voice bot

Call deflection means taking calls and deflecting them – having automation or self service handle the calls instead of getting them to human agents. In the case of CallJoy, a call comes in. message plays out to the user (“this call is being recorded”). User is asked if he wants to do something over a text message:

If the user is happy with that, then an SMS gets sent to the caller and he can continue from there.

There’s a voicebot here that handles the user’s answer (yes, yap, yes please, sure, …) and makes that decision. Nothing too fancy.

This part was probably implemented by using Google’s Dialogflow.

Today, the focus is on restaurants and in order-taking for the call deflection part. It can be used for other scenarios, but that’s the one Google is starting with:

Notice how there’s “LEARN MORE” only on restaurants? All other verticals in the examples on the CallJoy websites make use of the rest of CallJoy’s capabilities. Restaurants is the only one where call deflection is highlighted through an integration with a third party The Ordering.app, who are, for all intent and purpose an unknown vendor. Here’s what LinkedIn knows about them:

(one has to wonder how and why this partner was picked – and who’s cousin owns this company)

Anyways – call deflection now is done via SMS, and integration with a third party. Future releases will probably have more integrations and third parties to work with – and with that more use cases covered.

Another aspect in the future might be making a decision of where to route a user to – what link to send him based on his intent. This is something that happens in terms of a focus in larger businesses today in their automation initiatives.

#3 – Call transcription

This one seems like table stakes.

Transcription is the source of gaining insights from voice.

CallJoy offers transcription of all calls made.

The purpose? Enable analytics for the small business, which is based on tags and BI (below).

This most certainly makes use of Google’s speech to text service

#4- Automated tagging on call transcripts

It seems CallJoy offers tagging of the transcripts or finding specific keywords.

There’s not much explanation or information about tags, but it seems to work by specifying search words and these become tags across the recordings of calls that were made.

Identifying tags might be a manual process or an automated one (it isn’t really indicated anywhere). The intent here is to allow businesses to indicate what they are interested in (order, inventory, reservation, etc.).

#5- Metrics and dashboards

Then there’s the BI part – business intelligence.

Take the information collected, place it on nice dashboards to show the users.

This gives small businesses insights on who is calling them, when and for what purpose. Sounds trivial and obvious, but how many small businesses have that data today?

No machine learning or AI here – just old school BI. The main difference is that the data collected along with the insights gleaned make use of machine learning.

Sum it up

To sum things up, CallJoy uses transcription and makes basic use of Dialogflow to build a simple voicebot (probably single step – question+answer) and wraps it up in a solution that is pretty darn useful for businesses.

It does that for $39 a month per location. Very little to lose by trying it out…

A different route

Where most AI vendors are targeting large enterprises, Google decided to take the route of the small business. Trying to solve their problems. The challenge here is that there’s not enough data within a single business – and not enough money for running a data science project.

Google figured out how to cater for this audience with the tools they had at hand, without using the industry’s gold standard for call centers or try a fancy catch-all solution to answer and manage all calls.

The industry’s gold standard? An IVR. Get a person to menu-hell until he reaches what he needs.

Catch-all solution? Put an AI that can handle 90%+ if the call scenarios on its own automatically.

Both an IVR and mapping call scenarios means customizing the solution, which suggests longer onboarding with a more complicated solution. By taking the route of simplification Google made it possible to cater for small businesses.

A virtuous cycle

Google gains here twice.

Once by attracting small businesses to its service.

Twice by collecting these calls and the intents and tags businesses put. This ends up gaining more insights for Google, turning them into additional features, which later on attracts yet more businesses to a better CallJoy business.

It is all about automation

Here’s what you’ll find on the FAQ page of CallJoy:

With CallJoy, you’ll be able to:

  • Gain powerful insights with audio recordings and searchable text transcripts of all connected incoming calls.
  • Make better business decisions with metrics such as peak call times, new vs. returning callers, and conversation topics.
  • Easily direct callers via text message text to place an order or schedule an appointment online, increasing sales while freeing up your staff.

Most of it talks about improving a service by automating much of what takes place. Which is what the whole notion of AI and machine learning is with communications. Well… mostly. There are a few other areas like quality optimization.

The whole AI gold rush we see today in the communications space boils down to the next level of automation we’re getting into with communications. In many cases this is about machine helping humans and not really machine replacing humans – not for many of the use cases and interactions. That will probably come later  

Interested in AI in communications? Tomorrow I’ll be hosting a webinar with Chad Hart on this topic – join us:

Register to the webinar

The post Google CallJoy & the age of automation in communications appeared first on BlogGeek.me.

Google CallJoy & the age of automation in communications

Mon, 05/06/2019 - 12:00

ML/AI is coming to communications really fast. It is going to manifest is as automation in communications but also in other ways.

Me? I wanted to talk about automation and communications. But then Google released CallJoy, which was… automation and communications. And it shows where we’re headed quite clearly with a service that is butt simple, and yet… Google seems to be the first at it, at least when it comes to aiming for simplicity and a powerful MVP. Here’s where I took this article –

Ever since Google launched Duplex at I/O 2018 I’ve been wondering what’s next. Google came out with a new service called CallJoy – a kind of a voice assistant/agent for small businesses. Before I go into the age of automation and communications, let’s try to find out where machine learning and artificial intelligence can be found in CallJoy.

Interested in AI in communications? Tomorrow I’ll be hosting a webinar with Chad Hart on this topic – join us:

Register to the webinar

CallJoy and AI

What CallJoy does exactly?

From the CallJoy website, it looks that the following takes place: you subscribe for the service, pick a local phone number to use and you’re good to go.

When people call your business, they get greeted by a message (“this call is being recorded for whatever purposes” kind of a thing). Next, it can “share” information such as business hours and ask if the caller wants to do stuff over a web link instead of talking to a human. If a web link is what you want (think a “yes please” answer to whatever you hear on the phone when you call), then you’ll get an SMS with a URL. Otherwise, you’ll just get routed to the business’ “real” phone number to be handled by a human. All calls get recorded.

What machine learning aspects does this service use?

#1 – Block unwanted spam calls

Incoming spam calls can really harass small businesses. Being able to get less of these is always a blessing. It is also becoming a big issue in the US, one that brings a lot of attention and some attempts at solving it by carriers as well as other vendors.

I am not sure what blocking does Google do here and if it makes direct use of machine learning or not – it certainly can. The fact that all calls get handled by a chatbot means that there’s some kind of a “gating” process that a spam call needs to pass first. This in itself blocks at least some of them spam calls.

#2 – Call deflection, using a voice bot

Call deflection means taking calls and deflecting them – having automation or self service handle the calls instead of getting them to human agents. In the case of CallJoy, a call comes in. message plays out to the user (“this call is being recorded”). User is asked if he wants to do something over a text message:

If the user is happy with that, then an SMS gets sent to the caller and he can continue from there.

There’s a voicebot here that handles the user’s answer (yes, yap, yes please, sure, …) and makes that decision. Nothing too fancy.

This part was probably implemented by using Google’s Dialogflow.

Today, the focus is on restaurants and in order-taking for the call deflection part. It can be used for other scenarios, but that’s the one Google is starting with:

Notice how there’s “LEARN MORE” only on restaurants? All other verticals in the examples on the CallJoy websites make use of the rest of CallJoy’s capabilities. Restaurants is the only one where call deflection is highlighted through an integration with a third party The Ordering.app, who are, for all intent and purpose an unknown vendor. Here’s what LinkedIn knows about them:

(one has to wonder how and why this partner was picked – and who’s cousin owns this company)

Anyways – call deflection now is done via SMS, and integration with a third party. Future releases will probably have more integrations and third parties to work with – and with that more use cases covered.

Another aspect in the future might be making a decision of where to route a user to – what link to send him based on his intent. This is something that happens in terms of a focus in larger businesses today in their automation initiatives.

#3 – Call transcription

This one seems like table stakes.

Transcription is the source of gaining insights from voice.

CallJoy offers transcription of all calls made.

The purpose? Enable analytics for the small business, which is based on tags and BI (below).

This most certainly makes use of Google’s speech to text service

#4- Automated tagging on call transcripts

It seems CallJoy offers tagging of the transcripts or finding specific keywords.

There’s not much explanation or information about tags, but it seems to work by specifying search words and these become tags across the recordings of calls that were made.

Identifying tags might be a manual process or an automated one (it isn’t really indicated anywhere). The intent here is to allow businesses to indicate what they are interested in (order, inventory, reservation, etc.).

#5- Metrics and dashboards

Then there’s the BI part – business intelligence.

Take the information collected, place it on nice dashboards to show the users.

This gives small businesses insights on who is calling them, when and for what purpose. Sounds trivial and obvious, but how many small businesses have that data today?

No machine learning or AI here – just old school BI. The main difference is that the data collected along with the insights gleaned make use of machine learning.

Sum it up

To sum things up, CallJoy uses transcription and makes basic use of Dialogflow to build a simple voicebot (probably single step – question+answer) and wraps it up in a solution that is pretty darn useful for businesses.

It does that for $39 a month per location. Very little to lose by trying it out…

A different route

Where most AI vendors are targeting large enterprises, Google decided to take the route of the small business. Trying to solve their problems. The challenge here is that there’s not enough data within a single business – and not enough money for running a data science project.

Google figured out how to cater for this audience with the tools they had at hand, without using the industry’s gold standard for call centers or try a fancy catch-all solution to answer and manage all calls.

The industry’s gold standard? An IVR. Get a person to menu-hell until he reaches what he needs.

Catch-all solution? Put an AI that can handle 90%+ if the call scenarios on its own automatically.

Both an IVR and mapping call scenarios means customizing the solution, which suggests longer onboarding with a more complicated solution. By taking the route of simplification Google made it possible to cater for small businesses.

A virtuous cycle

Google gains here twice.

Once by attracting small businesses to its service.

Twice by collecting these calls and the intents and tags businesses put. This ends up gaining more insights for Google, turning them into additional features, which later on attracts yet more businesses to a better CallJoy business.

It is all about automation

Here’s what you’ll find on the FAQ page of CallJoy:

With CallJoy, you’ll be able to:

  • Gain powerful insights with audio recordings and searchable text transcripts of all connected incoming calls.
  • Make better business decisions with metrics such as peak call times, new vs. returning callers, and conversation topics.
  • Easily direct callers via text message text to place an order or schedule an appointment online, increasing sales while freeing up your staff.

Most of it talks about improving a service by automating much of what takes place. Which is what the whole notion of AI and machine learning is with communications. Well… mostly. There are a few other areas like quality optimization.

The whole AI gold rush we see today in the communications space boils down to the next level of automation we’re getting into with communications. In many cases this is about machine helping humans and not really machine replacing humans – not for many of the use cases and interactions. That will probably come later  

Interested in AI in communications? Tomorrow I’ll be hosting a webinar with Chad Hart on this topic – join us:

Register to the webinar

The post Google CallJoy & the age of automation in communications appeared first on BlogGeek.me.

Latest WebRTC Developer Tools Landscape (and report)

Mon, 04/29/2019 - 12:00

The landscape of WebRTC developer tools is ever-changing. Here’s where we are at now.

It was time. Over a year passed since last I’ve updated my WebRTC PaaS report. The main changes that occurred since December 2017?

While working on the report, there were a few things that I needed to do:

  1. Update all 21 vendors with relevant information. Some progressed more than others. Some haven’t made any significant changes.
  2. Refresh all references, links and information in the report, to fit the status of WebRTC in 2019
  3. Publicize the appendix on group calling architectures, to give room for a new appendix on Flow and Embedded – two trends that are taking shape
WebRTC Developer Tools landscape

A chapter in the report deals with the WebRTC Developer Tools landscape – the vendors, frameworks, products and services that developers can use when building their WebRTC applications. And that was from June 2017… a long time ago in WebRTC-time.

So I got that updated as well.

You can download the WebRTC Developer Tools landscape infographic.

Helping developers decide

A theme that occurs on a daily basis almost is people asking what to use for their project.

Someone asked about a PHP signaling server in 2017. That question was raised again this month. I got a kind of a similar question over email about Python. Others use one CPaaS vendor and want to switch to another (because they are unhappy about quality, support, pricing, …). Or they want to try and build the infrastructure on their own.

The WebRTC Index is there to cater for that need. Guide people through the process of finding the tools they can use. It is great, but it isn’t detailed enough in some cases – it gives you the list of vendors to research, but you still need to go and research them to check their feature list and capabilities.

That’s why I created my paid report – Choosing a WebRTC API Platform. This report covers the CPaaS vendors who has WebRTC capabilities. And now with the updated edition, it is again up to date with the most current information on all vendors.

Thinking of using a 3rd party?

Trying to determine a different vendor to use?

Want to know how committed a certain vendor is to his platform?

All that can be found in the report, in a way that is easily reachable and digestible.

The report is available at a discounted price until the end of April (only 2 days left).

If you want to learn more about the report, you can:

  1. Download the table of contents and introduction
  2. Check out Agora.io’s 4-pager from the report (each vendor profiled as such a 4-pager for it)
  3. Contact me to ask questions

You can purchase the report online.

Shout out to Agora.io

The reason that 4-pager from Agora.io is openly available is that they sponsored this report.

Agora.io is one of the interesting vendors in this space. They have their own network and coding technologies, and they hook it up to WebRTC. Their solution is also capable of dealing with live broadcasts at scale (think million viewers in a single video stream).

Check them out, and if you’re in San Francisco – attend their AllThingsRTC event.

The post Latest WebRTC Developer Tools Landscape (and report) appeared first on BlogGeek.me.

Latest WebRTC Developer Tools Landscape (and report)

Mon, 04/29/2019 - 12:00

The landscape of WebRTC developer tools is ever-changing. Here’s where we are at now.

It was time. Over a year passed since last I’ve updated my WebRTC PaaS report. The main changes that occurred since December 2017?

While working on the report, there were a few things that I needed to do:

  1. Update all 21 vendors with relevant information. Some progressed more than others. Some haven’t made any significant changes.
  2. Refresh all references, links and information in the report, to fit the status of WebRTC in 2019
  3. Publicize the appendix on group calling architectures, to give room for a new appendix on Flow and Embedded – two trends that are taking shape
WebRTC Developer Tools landscape

A chapter in the report deals with the WebRTC Developer Tools landscape – the vendors, frameworks, products and services that developers can use when building their WebRTC applications. And that was from June 2017… a long time ago in WebRTC-time.

So I got that updated as well.

You can download the WebRTC Developer Tools landscape infographic.

Helping developers decide

A theme that occurs on a daily basis almost is people asking what to use for their project.

Someone asked about a PHP signaling server in 2017. That question was raised again this month. I got a kind of a similar question over email about Python. Others use one CPaaS vendor and want to switch to another (because they are unhappy about quality, support, pricing, …). Or they want to try and build the infrastructure on their own.

The WebRTC Index is there to cater for that need. Guide people through the process of finding the tools they can use. It is great, but it isn’t detailed enough in some cases – it gives you the list of vendors to research, but you still need to go and research them to check their feature list and capabilities.

That’s why I created my paid report – Choosing a WebRTC API Platform. This report covers the CPaaS vendors who has WebRTC capabilities. And now with the updated edition, it is again up to date with the most current information on all vendors.

Thinking of using a 3rd party?

Trying to determine a different vendor to use?

Want to know how committed a certain vendor is to his platform?

All that can be found in the report, in a way that is easily reachable and digestible.

The report is available at a discounted price until the end of April (only 2 days left).

If you want to learn more about the report, you can:

  1. Download the table of contents and introduction
  2. Check out Agora.io’s 4-pager from the report (each vendor profiled as such a 4-pager for it)
  3. Contact me to ask questions

You can purchase the report online.

Shout out to Agora.io

The reason that 4-pager from Agora.io is openly available is that they sponsored this report.

Agora.io is one of the interesting vendors in this space. They have their own network and coding technologies, and they hook it up to WebRTC. Their solution is also capable of dealing with live broadcasts at scale (think million viewers in a single video stream).

Check them out, and if you’re in San Francisco – attend their AllThingsRTC event.

The post Latest WebRTC Developer Tools Landscape (and report) appeared first on BlogGeek.me.

Upcoming WebRTC events in 2019

Mon, 04/22/2019 - 12:00

Suddenly, there are so many good WebRTC events you can attend.

My kids are still young, and for some reason, still consider me somewhat important in their lives. It is great, but also sad – I found myself this year needing to decline so many good events to attend. Here’s a list of all the places that I am not going to be at, but you should if you’re interested in WebRTC

BTW – Some of these events are still in their call for papers stage – why not go as a speaker?

AllThingsRTC

URL: http://allthingsrtc.org/

When? 13 June

Where? San Francisco

Call for speakers: https://www.papercall.io/allthingsrtc

AllThingsRTC is hosted by Agora.io. The event they did in China a few years back was great (I haven’t attended but got good feedback about it), and this one is taking the right direction. They have room for more speakers – so be sure to add your name if you wish to present.

Sadly, I won’t be able to join this event as I am just finishing a family holiday in London.

CommCon 2019

URL: https://2019.commcon.xyz/

When? 7-11 July

Where? Buckinghamshire, UK

CommCon started last year by Dan Jenkins from Nimble Ape.

It takes a view of the communications market as a whole from the point of view of the developers in that market. The event runs in two tracks with a good deal of sessions around WebRTC.

I couldn’t attend last year’s even and can’t attend this year’s event (extended family trip to Eastern Europe). What I’ve heard from last year’s attendees was that the event was really good – and as testament, the people I know are going to attend this year’s event as well.

ClueCon

URL: https://www.cluecon.com/

When? 5-8 August

Where? Downtown Chicago

Call for speakers: https://www.cluecon.com/speakers/

This is the 15th year that ClueCon will be held. This event is about open source projects in VoIP, with the team behind the event being the FreeSWITCH team.

This one is just after that extended family trip to Eastern Europe, and I’d rather not be on another airplane so soon.

Twilio Signal

URL: https://signal.twilio.com/

When? 6-7 August

Where? San Francisco

Call for speakers: https://eegeventsite.secure.force.com/twiliosignal/twiliosignalcfpreghome

Twilio Signal is a lot of fun. Twilio is the biggest CPaaS vendor out there and their event is quite large. I’ve been to two such events and found them really interesting. They deal a lot about Twilio products and new launches which tend to define a lot of the industry, but they have technical and business sessions as well.

Can’t make it this year. Falls at roughly the same time as ClueCon which I am skipping as well.

JanusCon

URL: https://www.januscon.it/

When? 23-25 September

Where? Napoli, Italy

Call for papers: https://www.papercall.io/januscon2019

The meetecho team behind Janus decided to create a conference around Janus.

Janus is one of the most popular open source WebRTC media servers today, and this is a leap of faith when it comes to creating an event – always a risky business.

I might end up attending it. For Janus (and for the food obviously). Only challenge is my daughter is starting a new school that month, so need to see if and how will that fit.

IIT RTC

URL: https://www.rtc-conference.com/2019/

When? 14-16 October

Where? Chicago

Call for speakers: https://www.rtc-conference.com/2019/submit-presentation-for-conference/

The IIT RTC is a mixture of academic and industry event around real time communications. I’ve taken part in it twice without really being there in person, through a video conference session. The event runs multiple tracks with WebRTC in a track of its own. As with many of the other larger industry events, IIT RTC is preceded by a TADHack event and one of its tracks is TAD Summit.

I’ll be skipping this one due to Sukkot holiday here in Israel.

Kranky Geek

URL: https://www.krankygeek.com/

When? 15 November

Where? San Francisco

Call for speakers: just contact me

That’s the event I am hosting with Chris Koehncke and Chad Hart. Our focus is WebRTC and ML/AI in real time communications. We’re still figuring out the sponsors and agenda for this year (just started planning the event).

Obviously, I’ll be attending this event…

Which event should you attend?

This is a question I’ve been asked quite a few times, and somehow, this year, there are just so many of them that I want and can’t attend. If you think of going to an event to learn about WebRTC and communications in general, then any of these will be great.

Go to a few – why settle for one?

Next Month

Next month, I’ll be hosting a webinar along with Chad Hart. We will be reviewing the changing domain of machine learning and artificial intelligence in real time communications. We’ve published a report about it a few months back, and it is time to take another look at the topic. If you’re interested – join us.

The post Upcoming WebRTC events in 2019 appeared first on BlogGeek.me.

Upcoming WebRTC events in 2019

Mon, 04/22/2019 - 12:00

Suddenly, there are so many good WebRTC events you can attend.

My kids are still young, and for some reason, still consider me somewhat important in their lives. It is great, but also sad – I found myself this year needing to decline so many good events to attend. Here’s a list of all the places that I am not going to be at, but you should if you’re interested in WebRTC

BTW – Some of these events are still in their call for papers stage – why not go as a speaker?

AllThingsRTC

URL: http://allthingsrtc.org/

When? 13 June

Where? San Francisco

Call for speakers: https://www.papercall.io/allthingsrtc

AllThingsRTC is hosted by Agora.io. The event they did in China a few years back was great (I haven’t attended but got good feedback about it), and this one is taking the right direction. They have room for more speakers – so be sure to add your name if you wish to present.

Sadly, I won’t be able to join this event as I am just finishing a family holiday in London.

CommCon 2019

URL: https://2019.commcon.xyz/

When? 7-11 July

Where? Buckinghamshire, UK

CommCon started last year by Dan Jenkins from Nimble Ape.

It takes a view of the communications market as a whole from the point of view of the developers in that market. The event runs in two tracks with a good deal of sessions around WebRTC.

I couldn’t attend last year’s even and can’t attend this year’s event (extended family trip to Eastern Europe). What I’ve heard from last year’s attendees was that the event was really good – and as testament, the people I know are going to attend this year’s event as well.

ClueCon

URL: https://www.cluecon.com/

When? 5-8 August

Where? Downtown Chicago

Call for speakers: https://www.cluecon.com/speakers/

This is the 15th year that ClueCon will be held. This event is about open source projects in VoIP, with the team behind the event being the FreeSWITCH team.

This one is just after that extended family trip to Eastern Europe, and I’d rather not be on another airplane so soon.

Twilio Signal

URL: https://signal.twilio.com/

When? 6-7 August

Where? San Francisco

Call for speakers: https://eegeventsite.secure.force.com/twiliosignal/twiliosignalcfpreghome

Twilio Signal is a lot of fun. Twilio is the biggest CPaaS vendor out there and their event is quite large. I’ve been to two such events and found them really interesting. They deal a lot about Twilio products and new launches which tend to define a lot of the industry, but they have technical and business sessions as well.

Can’t make it this year. Falls at roughly the same time as ClueCon which I am skipping as well.

JanusCon

URL: https://www.januscon.it/

When? 23-25 September

Where? Napoli, Italy

Call for papers: https://www.papercall.io/januscon2019

The meetecho team behind Janus decided to create a conference around Janus.

Janus is one of the most popular open source WebRTC media servers today, and this is a leap of faith when it comes to creating an event – always a risky business.

I might end up attending it. For Janus (and for the food obviously). Only challenge is my daughter is starting a new school that month, so need to see if and how will that fit.

IIT RTC

URL: https://www.rtc-conference.com/2019/

When? 14-16 October

Where? Chicago

Call for speakers: https://www.rtc-conference.com/2019/submit-presentation-for-conference/

The IIT RTC is a mixture of academic and industry event around real time communications. I’ve taken part in it twice without really being there in person, through a video conference session. The event runs multiple tracks with WebRTC in a track of its own. As with many of the other larger industry events, IIT RTC is preceded by a TADHack event and one of its tracks is TAD Summit.

I’ll be skipping this one due to Sukkot holiday here in Israel.

Kranky Geek

URL: https://www.krankygeek.com/

When? 15 November

Where? San Francisco

Call for speakers: just contact me

That’s the event I am hosting with Chris Koehncke and Chad Hart. Our focus is WebRTC and ML/AI in real time communications. We’re still figuring out the sponsors and agenda for this year (just started planning the event).

Obviously, I’ll be attending this event…

Which event should you attend?

This is a question I’ve been asked quite a few times, and somehow, this year, there are just so many of them that I want and can’t attend. If you think of going to an event to learn about WebRTC and communications in general, then any of these will be great.

Go to a few – why settle for one?

Next Month

Next month, I’ll be hosting a webinar along with Chad Hart. We will be reviewing the changing domain of machine learning and artificial intelligence in real time communications. We’ve published a report about it a few months back, and it is time to take another look at the topic. If you’re interested – join us.

The post Upcoming WebRTC events in 2019 appeared first on BlogGeek.me.

WebRTC Multiparty Architectures

Mon, 04/15/2019 - 12:00

There are multiple ways to implement WebRTC multiparty sessions. These in turn are built around mesh, mixing and routing.

In the past few days I’ve been sick to the bone. Fever, headache, cough – the works. I couldn’t do much which meant no writing an article either. Good thing I had to remove an appendix from my upcoming WebRTC API Platforms report to make room for a new one.

I wanted to touch the topic of Flow and Embed in Communication APIs, and how they fit into the WebRTC space. This topic will replace an appendix in the report about multiparty architectures in WebRTC, which is what follows here – a copy+paste of that appendix:

Multiparty conferences of either voice or video can be supported in one of three ways:

  1. Mesh
  2. Mixing
  3. Routing

The quality of the solution will rely heavily on the different type of architecture used. In Routing, we see further refinement for video routing between multi-unicast, simulcast and SVC.

WebRTC API Platform vendors who offer multiparty conferencing will have different implementations of this technology. For those who need multiparty calling, make sure you know which technology is used by the vendor you choose.

Mesh

In a mesh architecture, all users are connected to all others directly and send their media to them. While there is no overhead on a media server, this option usually falls short of offering any meaningful media quality and starts breaking from 4 users or more.

Mesh topology

For the most part, consider vendors offering mesh topology for their video service as limited at best.

Mixing

MCUs were quite common before WebRTC came into the market. MCU stands for Multipoint Conferencing Unit, and it acts as a mixing point.

MCU mixing topology

An MCU receives the incoming media streams from all users, decodes it all, creates a new layout of everything and sends it out to all users as a single stream.

This has the added benefit of being easy on the user devices, which see it as a single user they need to operate in front; but it comes at a high compute cost and an inflexibility on the user side.

Routing

SFUs were new before WebRTC, but are now an extremely popular solution. SFU stands for Selective Forwarding Unit, and it acts like a router of media.

SFU routing topology

An SFU receives the incoming media streams from all users, and then decides which streams to send to which users.

This approach leaves flexibility on the user side while reducing the computational cost on the server side; making it the popular and cost effective choice in WebRTC deployments.

To route media, an SFU can employ one of three distinct approaches:

  1. Multi-unicast
  2. Simulcast
  3. SVC
Multi-unicast

This is the naïve approach to routing media. Each user sends his video stream towards he SFU, which then decide who to route this stream to.

If there is a need to lower bitrates or resolutions, it is either done at the source, by forcing a user to change his sent stream, or on the receiver end, by having the receiving user to throw data he received and processed.

It is also how most implementations of WebRTC SFUs were done until recently. [UPDATE: Since this article was originally written in 2017, that was true. In 2019, most are actually using Simulcast] Simulcast

Simulcast is an approach where the user sends multiple video streams towards the SFU. These streams are compressed data of the exact same media, but in different quality levels – usually different resolutions and bitrates.

Simulcast

The SFU can then select which of the streams it received to send to which participant based on their device capability, available network or screen layout.

Simulcast has started to crop in commercial WebRTC SFUs only recently.

SVC

SVC stands for Scalable Video Coding. It is a technique where a single encoded video stream is created in a layered fashion, where each layer adds to the quality of the previous layer.

SVC

When an SFU receives a media stream that uses SVC, it can peel of layers out of that stream, to fit the outgoing stream to the quality, device, network and UI expectations of the receiving user. It offers better performance than Simulcast in both compute and network resources.

SVC has the added benefit of enabling higher resiliency to network impairments by allowing adding error correction only to base layers. This works well over mobile networks even for 1:1 calling.

SVC is very new to WebRTC and is only now being introduced as part of the VP9 video codec.

The post WebRTC Multiparty Architectures appeared first on BlogGeek.me.

WebRTC Multiparty Architectures

Mon, 04/15/2019 - 12:00

There are multiple ways to implement WebRTC multiparty sessions. These in turn are built around mesh, mixing and routing.

In the past few days I’ve been sick to the bone. Fever, headache, cough – the works. I couldn’t do much which meant no writing an article either. Good thing I had to remove an appendix from my upcoming WebRTC API Platforms report to make room for a new one.

I wanted to touch the topic of Flow and Embed in Communication APIs, and how they fit into the WebRTC space. This topic will replace an appendix in the report about multiparty architectures in WebRTC, which is what follows here – a copy+paste of that appendix:

Multiparty conferences of either voice or video can be supported in one of three ways:

  1. Mesh
  2. Mixing
  3. Routing

The quality of the solution will rely heavily on the different type of architecture used. In Routing, we see further refinement for video routing between multi-unicast, simulcast and SVC.

WebRTC API Platform vendors who offer multiparty conferencing will have different implementations of this technology. For those who need multiparty calling, make sure you know which technology is used by the vendor you choose.

Mesh

In a mesh architecture, all users are connected to all others directly and send their media to them. While there is no overhead on a media server, this option usually falls short of offering any meaningful media quality and starts breaking from 4 users or more.

Mesh topology

For the most part, consider vendors offering mesh topology for their video service as limited at best.

Mixing

MCUs were quite common before WebRTC came into the market. MCU stands for Multipoint Conferencing Unit, and it acts as a mixing point.

MCU mixing topology

An MCU receives the incoming media streams from all users, decodes it all, creates a new layout of everything and sends it out to all users as a single stream.

This has the added benefit of being easy on the user devices, which see it as a single user they need to operate in front; but it comes at a high compute cost and an inflexibility on the user side.

Routing

SFUs were new before WebRTC, but are now an extremely popular solution. SFU stands for Selective Forwarding Unit, and it acts like a router of media.

SFU routing topology

An SFU receives the incoming media streams from all users, and then decides which streams to send to which users.

This approach leaves flexibility on the user side while reducing the computational cost on the server side; making it the popular and cost effective choice in WebRTC deployments.

To route media, an SFU can employ one of three distinct approaches:

  1. Multi-unicast
  2. Simulcast
  3. SVC
Multi-unicast

This is the naïve approach to routing media. Each user sends his video stream towards he SFU, which then decide who to route this stream to.

If there is a need to lower bitrates or resolutions, it is either done at the source, by forcing a user to change his sent stream, or on the receiver end, by having the receiving user to throw data he received and processed.

It is also how most implementations of WebRTC SFUs were done until recently.

Simulcast

Simulcast is an approach where the user sends multiple video streams towards the SFU. These streams are compressed data of the exact same media, but in different quality levels – usually different resolutions and bitrates.

Simulcast

The SFU can then select which of the streams it received to send to which participant based on their device capability, available network or screen layout.

Simulcast has started to crop in commercial WebRTC SFUs only recently.

SVC

SVC stands for Scalable Video Coding. It is a technique where a single encoded video stream is created in a layered fashion, where each layer adds to the quality of the previous layer.

SVC

When an SFU receives a media stream that uses SVC, it can peel of layers out of that stream, to fit the outgoing stream to the quality, device, network and UI expectations of the receiving user. It offers better performance than Simulcast in both compute and network resources.

SVC has the added benefit of enabling higher resiliency to network impairments by allowing adding error correction only to base layers. This works well over mobile networks even for 1:1 calling.

SVC is very new to WebRTC and is only now being introduced as part of the VP9 video codec.

The post WebRTC Multiparty Architectures appeared first on BlogGeek.me.

Handling session disconnections in WebRTC

Mon, 04/08/2019 - 12:00

WebRTC disconnections are quite common, but you can “fix” many of them just by careful planning and proper development.

Years ago, I developed the H.323 Protocol Stack at RADVISION (later turned Avaya, turned Spirent turned Softil). I was there as a developer, R&D manager and then the product manager. My code is probably still in that codebase, lovingly causing products around the globe to crash from time to time – as any other developer, I have my share of bugs left behind.

Anyways, why am I mentioning this?

I had a client asking me recently about disconnections in WebRTC. And it kinda reminded me of a similar issue (or set of issues) we had with the H.323 stack and protocol years back.

If you bear with me a bit – I promise it will be worth your while.

I am starting this week the office hours for my WebRTC course. The next office hour (after the initial “hi everyone”) will cover WebRTC disconnections.

Check out the course – and maybe go over the first module for free:

Learn WebRTC

A quick intro to H.323 signaling and transport

H.323 is like SIP just better and more complex. At least for me, who started his way in VoIP with H.323 (I will always have a soft spot for it). For many years, the way H.323 worked is by opening two separate TCP connections for transporting its signaling. The first for passing what is called Q.931 protocol and the next for passing H.245 protocol.

If you would like to compare it to the way WebRTC handles things, then Q.931 is how you setup the connection – have the users find each other. H.245 is similar to what SDP and JSEP are for (I am blatantly ignoring H.225 here, another protocol in H.323 which takes care of registration and authentication).

Once Q.931 and H.245 get connected, you start adding the RTP/RTCP stuff over UDP, which gets you quite a lot of connections.

Add to that complexities like tunneling H.245 over Q.931, using something called faststart instead of H.245 (or before H.245), then sprinkle a dash of “parallel H.245” and then a bit of NAT traversal and/or security and you get a lot of places that require testing and a huge number of edge cases.

Where can H.323 get “stuck” or disconnected?

With so many connections, there are a lot of places that things can go wrong. There are multiple state machines (one for Q.931 state, one for H.245 state) and there are different connections that can get severed for one reason or another.

Oh – and in H.323 (at least in its earlier specifications that I had the joy to work with), when the Q.931 or H.245 connections get severed – the whole session is considered as disconnected, so you go and kill the RTP/RTCP sessions.

At the time, we suffered a lot from zombie sessions due to different edge cases. We ended up with solutions that were either based on the H.323 specification itself or best practices we created along the way.

Here are a few of these:

  • If the Q.931 connection gets severed – kill the session
  • If the H.245 connection gets severed – kill the session
  • If you don’t receive media or media control packets on RTP or RTCP respectively for a configurable period of time (think 5-10 seconds) – kill the session
  • When a state machine for Q.931 or H.245 initiates – start a timer. If that timer ends and the state machine didn’t get to the connected state – switch the state to timeout and… – kill the session
  • Killing the session means trying to gracefully close all connections, but if we can’t within a short period of a timeout – we just shut things down to collect the resources back to be used later

H.323 existed before smartphones. Systems were usually tethered to an ethernet cable or at most over WiFi in a static location at a time. There was no notion of roaming or moving between networks. Which meant that there was no need to ask yourself if a connection got severed because of a switch in the network or because there’s a real issue.

Life was simple:

And if you were really insistent then maybe this:

(in real life scenarios, these two simplistic state machines were a lot bigger and complicated, but their essence was based on these concepts)

Back to WebRTC signaling and transport

WebRTC is simpler and more complicated than H.323 at the same thing.

It is simpler, as there is only SRTP. There’s no signaling that is standardized or preselected for WebRTC. And for the most part, the one you use will probably require only a single connection (as opposed to the two in H.323). It also has a lot less alternatives built into the specification itself that H.323 has.

It is more complicated, as you own the signaling part. You make that selection, so you better make a good one. And while at it, implement it reasonably well and handle all of its edge cases. This is never a simple task even for simple signaling protocols. And it’s now on you.

Then there’s the fact that networks today are more complex. User expect to move around while communicating, and you should expect such scenarios where users switch networks in mid-session.

If you use WebRTC in a browser, then you get these interesting aspects associated with your implementation:

  1. When you close the browser, the session dies
  2. When you close the tab where the WebRTC session lives, the session dies
  3. When you refresh the page where the WebRTC session lives, the session dies
  4. When you click a link to move to a different page (even on the same site), the session dies

A lot of dying taking place on the browser, and the server, or the other client, will need to “sniff” these scenarios as they might not be gracefully disconnected, and decide what to do about them.

Where can WebRTC get “stuck” or disconnected?

We can split disconnections of WebRTC into 3 broad categories:

  1. Failure to connect at all
  2. Media disconnections
  3. Signaling disconnections

In each, there will be multiple scenarios, defining the reasons for failure as well as how to handle and overcome such issues.

In broad strokes, here’s what I’d do in each of these 3 categories:

#1 – Failure to connect at all

There’s a decent amount of failures happening when trying to connect WebRTC sessions. They start from not being able to even send out an SDP, through interoperability issues across browsers and devices to ICE negotiation failing to connect media.

In many of these cases, better configuration of the service as well as focus on edge cases would improve the situation.

If you experience connection failures for 10% or more of the sessions – you’re doing something wrong. Some can get it as low as 1% or less, but oftentimes that depends on the type of users your service attracts.

This leads to another very important aspect of using WebRTC:

Measure what you can if you want to be able to improve it in the future

#2 – Media disconnections

Sometimes, your sessions will simply disconnect.

There are many reasons why that can happen:

  • The firewall policies of the access point used are configured to kill P2P encrypted traffic (blame all them bittorrent-hating-IT-people)
  • The user switched from one network to another in mid-session, and you should follow WebRTC’s ICE restart mechanism
  • The other end crashed, closed or just got offline

Each of these requires different handling – some in the code while others some manual handling (think customer support working out the configuration with a customer to resolve the firewall issue).

#3 – Signaling disconnections

Unlike H.323, if signaling gets disconnected, WebRTC doesn’t even know about it, so it won’t immediately cause the session itself to disconnect.

First thing you’ll need to do is make a decision how you want to proceed in such cases – do you treat this as session failure/disconnection or do you let the show go on.

If you treat these as failures, then I suggest killing peer connections based on the status of your websocket connection to the server. If you are on the server side, then once a connection is lost, you should probably go ahead and kill the media paths – either from your media server towards the “dead” session leg or from the other participant on a P2P connection/session.

If you want to make sure the show goes on, you will need to try and reconnect the peer connection towards the same user/session somehow. In which case, additional signaling logic in your connection state machine along with additional timers to manage it will be necessary.

Announcing the WebRTC course snippets module

Here’s the thing.

My online WebRTC training has everything in it already. Well… not everything, but it is rather complete. What I’ve noticed is that I get repeat questions from different students and clients on very specific topics. They are mostly covered within lessons of the course, but they sometimes feel as being “buried” within the hours and hours of content.

This is why I decided to start creating course snippets. These are “lessons” that are 3-5 minutes long (as opposed to 20-40 minutes long), with a purpose to give an answer to one specific question at a time. Most of the snippets will be actionable and may contain additional materials to assist you in your development. This library of snippets will make up a new course module.

Here are the first 3 snippets that will be added:

  1. WebRTC session disconnections
  2. ICE servers configuration
  3. A Quick review of QUIC

While we’re at it, office hours for the course start today. If you want to learn WebRTC, now is the best time to enroll.

The post Handling session disconnections in WebRTC appeared first on BlogGeek.me.

Handling session disconnections in WebRTC

Mon, 04/08/2019 - 12:00

WebRTC disconnections are quite common, but you can “fix” many of them just by careful planning and proper development.

Years ago, I developed the H.323 Protocol Stack at RADVISION (later turned Avaya, turned Spirent turned Softil). I was there as a developer, R&D manager and then the product manager. My code is probably still in that codebase, lovingly causing products around the globe to crash from time to time – as any other developer, I have my share of bugs left behind.

Anyways, why am I mentioning this?

I had a client asking me recently about disconnections in WebRTC. And it kinda reminded me of a similar issue (or set of issues) we had with the H.323 stack and protocol years back.

If you bear with me a bit – I promise it will be worth your while.

I am starting this week the office hours for my WebRTC course. The next office hour (after the initial “hi everyone”) will cover WebRTC disconnections.

Check out the course – and maybe go over the first module for free:

Learn WebRTC

A quick intro to H.323 signaling and transport

H.323 is like SIP just better and more complex. At least for me, who started his way in VoIP with H.323 (I will always have a soft spot for it). For many years, the way H.323 worked is by opening two separate TCP connections for transporting its signaling. The first for passing what is called Q.931 protocol and the next for passing H.245 protocol.

If you would like to compare it to the way WebRTC handles things, then Q.931 is how you setup the connection – have the users find each other. H.245 is similar to what SDP and JSEP are for (I am blatantly ignoring H.225 here, another protocol in H.323 which takes care of registration and authoentication).

Once Q.931 and H.245 get connected, you start adding the RTP/RTCP stuff over UDP, which gets you quite a lot of connections.

Add to that complexities like tunneling H.245 over Q.931, using something called faststart instead of H.245 (or before H.245), then sprinkle a dash of “parallel H.245” and then a bit of NAT traversal and/or security and you get a lot of places that require testing and a huge number of edge cases.

Where can H.323 get “stuck” or disconnected?

With so many connections, there are a lot of places that things can go wrong. There are multiple state machines (one for Q.931 state, one for H.245 state) and there are different connections that can get severed for one reason or another.

Oh – and in H.323 (at least in its earlier specifications that I had the joy to work with), when the Q.931 or H.245 connections get severed – the whole session is considered as disconnected, so you go and kill the RTP/RTCP sessions.

At the time, we suffered a lot from zombie sessions due to different edge cases. We ended up with solutions that were either based on the H.323 specification itself or best practices we created along the way.

Here are a few of these:

  • If the Q.931 connection gets severed – kill the session
  • If the H.245 connection gets severed – kill the session
  • If you don’t receive media or media control packets on RTP or RTCP respectively for a configurable period of time (think 5-10 seconds) – kill the session
  • When a state machine for Q.931 or H.245 initiates – start a timer. If that timer ends and the state machine didn’t get to the connected state – switch the state to timeout and… – kill the session
  • Killing the session means trying to gracefully close all connections, but if we can’t within a short period of a timeout – we just shut things down to collect the resources back to be used later

H.323 existed before smartphones. Systems were usually tethered to an ethernet cable or at most over WiFi in a static location at a time. There was no notion of roaming or moving between networks. Which meant that there was no need to ask yourself if a connection got severed because of a switch in the network or because there’s a real issue.

Life was simple:

And if you were really insistent then maybe this:

(in real life scenarios, these two simplistic state machines were a lot bigger and complicated, but their essence was based on these concepts)

Back to WebRTC signaling and transport

WebRTC is simpler and more complicated than H.323 at the same thing.

It is simpler, as there is only SRTP. There’s no signaling that is standardized or preselected for WebRTC. And for the most part, the one you use will probably require only a single connection (as opposed to the two in H.323). It also has a lot less alternatives built into the specification itself that H.323 has.

It is more complicated, as you own the signaling part. You make that selection, so you better make a good one. And while at it, implement it reasonably well and handle all of its edge cases. This is never a simple task even for simple signaling protocols. And it’s now on you.

Then there’s the fact that networks today are more complex. User expect to move around while communicating, and you should expect such scenarios where users switch networks in mid-session.

If you use WebRTC in a browser, then you get these interesting aspects associated with your implementation:

  1. When you close the browser, the session dies
  2. When you close the tab where the WebRTC session lives, the session dies
  3. When you refresh the page where the WebRTC session lives, the session dies
  4. When you click a link to move to a different page (even on the same site), the session dies

A lot of dying taking place on the browser, and the server, or the other client, will need to “sniff” these scenarios as they might not be gracefully disconnected, and decide what to do about them.

Where can WebRTC get “stuck” or disconnected?

We can split disconnections of WebRTC into 3 broad categories:

  1. Failure to connect at all
  2. Media disconnections
  3. Signaling disconnections

In each, there will be multiple scenarios, defining the reasons for failure as well as how to handle and overcome such issues.

In broad strokes, here’s what I’d do in each of these 3 categories:

#1 – Failure to connect at all

There’s a decent amount of failures happening when trying to connect WebRTC sessions. They start from not being able to even send out an SDP, through interoperability issues across browsers and devices to ICE negotiation failing to connect media.

In many of these cases, better configuration of the service as well as focus on edge cases would improve the situation.

If you experience connection failures for 10% or more of the sessions – you’re doing something wrong. Some can get it as low as 1% or less, but oftentimes that depends on the type of users your service attracts.

This leads to another very important aspect of using WebRTC:

Measure what you can if you want to be able to improve it in the future

#2 – Media disconnections

Sometimes, your sessions will simply disconnect.

There are many reasons why that can happen:

  • The firewall policies of the access point used are configured to kill P2P encrypted traffic (blame all them bittorrent-hating-IT-people)
  • The user switched from one network to another in mid-session, and you should follow WebRTC’s ICE restart mechanism
  • The other end crashed, closed or just got offline

Each of these requires different handling – some in the code while others some manual handling (think customer support working out the configuration with a customer to resolve the firewall issue).

#3 – Signaling disconnections

Unlike H.323, if signaling gets disconnected, WebRTC doesn’t even know about it, so it won’t immediately cause the session itself to disconnect.

First thing you’ll need to do is make a decision how you want to proceed in such cases – do you treat this as session failure/disconnection or do you let the show go on.

If you treat these as failures, then I suggest killing peer connections based on the status of your websocket connection to the server. If you are on the server side, then once a connection is lost, you should probably go ahead and kill the media paths – either from your media server towards the “dead” session leg or from the other participant on a P2P connection/session.

If you want to make sure the show goes on, you will need to try and reconnect the peer connection towards the same user/session somehow. In which case, additional signaling logic in your connection state machine along with additional timers to manage it will be necessary.

Announcing the WebRTC course snippets module

Here’s the thing.

My online WebRTC training has everything in it already. Well… not everything, but it is rather complete. What I’ve noticed is that I get repeat questions from different students and clients on very specific topics. They are mostly covered within lessons of the course, but they sometimes feel as being “buried” within the hours and hours of content.

This is why I decided to start creating course snippets. These are “lessons” that are 3-5 minutes long (as opposed to 20-40 minutes long), with a purpose to give an answer to one specific question at a time. Most of the snippets will be actionable and may contain additional materials to assist you in your development. This library of snippets will make up a new course module.

Here are the first 3 snippets that will be added:

  1. WebRTC session disconnections
  2. ICE servers configuration
  3. A Quick review of QUIC

While we’re at it, office hours for the course start today. If you want to learn WebRTC, now is the best time to enroll.

The post Handling session disconnections in WebRTC appeared first on BlogGeek.me.

CPaaS differentiation in 2019

Mon, 04/01/2019 - 12:00

CPaaS differentiation seems to be revolving around tackling niches.

Time for another look at the world of CPaaS – Communication Platform as a Service. In January 2018, a bit over a year ago, I’ve looked at CPaaS trends for 2018. The ones there were:

  1. Serverless – which didn’t really happen, at least not as a direct CPaaS offering, other than what Twilio has to offer and what Voximplant had as well
  2. Omnichannel – where we see most vendors collecting channels to support, with Whatsapp being the lead noise-maker
  3. Visual/IDE – ended up being a winner in 2018, with Plivo, MessageBird, Voximplant and Infobip joining Twilio. It is also now usually called “Flow”
  4. Machine learning and AI – still more talk than action, but we’re moving in this direction. The whole industry is
  5. AR/VR – happening, though less with the CPaaS vendors directly
  6. Bots – that’s part of the omnichannel + ML/AI story. And we see instances of it done with CPaaS
  7. GDPR – something that was done and somehow mostly forgotten

I’d like to look at what’s happening in CPaaS this time from a slightly different angle, which alludes itself to trends as well, but in a more nuanced way. From briefings I’ve been given this past few weeks and the announcements and stories coming out of Enterprise Connect 2019, it looks like different CPaaS vendors are settling on different target audiences and catering to different use cases and market niches.

Today CPaaS is almost synonymous to Twilio. Every player looks at what Twilio does in order to plot its own route in the market, at times, with the intended aim of disrupting Twilio and then mostly with lower price points. In other times, with trying to offer something more/better.

Then there are external players who add APIs to their platform. Usually UCaaS (Unified Communications as a Service) platform. They don’t directly compete with CPaaS, but if you are purchasing a “phone system” for your enterprise from a UCaaS player, then why not use its APIs and services instead of opting for another vendor (a CPaaS vendor in this case)?

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Here are how some of the vendors in this space are trying to differentiate, pivot and/or find their niche within the CPaaS market.

Agora.io – Gaming

If you look at Agora’s blog, what you’ll find out there is a slew of posts around gaming and gaming related frameworks (Unity to be exact):

  • It’s How You Play the Game: Trends at Game Developers Conference – Day 1 Recap
  • Adding Voice Chat to a Multiplayer Cross-Platform Unity game
  • How To: Create a Video Chat App in Unity
  • Add Voice Chat to your Unity game
  • (iOS) Run Video Chat within your Unity application
  • (Android) Run Video Chat within your Unity application
Agora offers a specific solution for gaming

Gaming is an untapped market for CPaaS.

There’s communications there of all kinds – collaboration or communications across gamers inside a game, talking before the game, streaming the game to viewers, etc.

All this communications is either developed by the gaming companies (not a lot), being catered for by specialized VoIP gaming vendors, done out of scope (using Discord, Skype, …). Rarely is it covered by a CPaaS vendor.

Somehow, for CPaaS cracking this market is really tough. Agora.io is trying to do just that, along with its other focus areas – live broadcast and social (two other tough nuts).

ECLWebRTC – Media Pipeline

The Japanese platform from NTT Communications – ECLWebRTC.

Like many of the WebRTC-first/only platforms out there, ECLWebRTC had an SFU implementation and support for various devices and browsers.

When you get to that point, one approach is to go after voice and PSTN. Another one is to add more features and increase the sizes of meetings and live broadcasts that can be supported.

ECLWebRTC decided to go after machine learning here, with the intent of letting its customers integrate and connect its media paths directly to cloud APIs. This is done using what they call Media Pipeline Factory, which feels from the looks of it like a general purpose media server.

ECLWebRTC is less known in Europe and the US, and probably not much outside of Japan either. With the Japanese market focus on automation, it makes sense that media pipeline would be a focus area for ECLWebRTC. This type of a capability is relevant elsewhere as well, but it doesn’t seem to be a priority for others yet.

Infobip – Omnichannel

I’ve had the opportunity to fiddle around with Infobip Flow recently, something that turned out to be a very pleasant experience. From Flow, it became apparent that Infobip is working hard on offering its customers an omnichannel experience. Compared to other CPaaS vendors, they seem to have the most coverage of channels:

To the above, you can add SMS and RCS and email.

Infobip Flow has another nice  quality – it is built for both inbound and outbound communications. Most of its competitors do inbound flows only.

In a world where competition may force price wars on CPaaS basic offerings of voice and SMS, adding support for omnichannel seems like a good way to limit attrition and churn and increase vendor lock-in.

RingCentral – Embeddables

RingCentral isn’t a CPaaS vendor. They offer a communication service for the enterprise. You got a company and need a way to communicate? There’s RingCentral.

What they’ve done in the past couple of years was add an API layer to some of their services. Things like pushing messages into Glip, handling phone calls, etc.

The idea is that if you need something done in an automated fashion in RingCentral you can use the API for it. In many simple cases, this might be used instead of adopting CPaaS APIs. in other cases, it is about using a single vendor or having specific integrations relevant to the RingCentral platform.

What RingCentral did was add what they call Embeddable:

“With RingCentral Embeddable, you can embed a full-featured softphone into your favorite web application for an integrated communications experience that drives productivity and ease of use without lengthy development time“

This concept of embedding a piece of code isn’t new – YouTube videos offer such a capability as well as a slew of other services out there. When it comes to communications, it is similar in nature to what TokBox has in the form of Video Chat Embeds but done at the level of users and their user accounts on RingCentral.

This definitely makes integrations of RingCentral with CRM tools a lot easier to get done, and makes it easier to non-developers to engage with them – similar to how Flow type offerings make it easier for non-developers to handle communication flows.

SignalWire – Price and Flexibility

SignalWire is an interesting proposition. It comes from the team that created and is maintaining FreeSWITCH, the leading open source framework used today by many communication providers, including some of the CPaaS vendors.

The FreeSWITCH team decided to build their own managed service (=CPaaS in this case), calling it SignalWire. Here’s a few examples of the punchy copy they have on their website:

  • Advanced communications from the source
  • We don’t price gouge you for carrier services like per-minute and per-message rates. Focus on what’s important to your business, not your phone bill

What they seem to be aiming for are two things: price and flexibility

Price

They offer close to whole-sale price points (at least based on the website – I haven’t gone to a price comparison on this one, though their sample pricing for the US does seem low).

To make things easier, they are targeting Twilio customers, doing that by offering TwiML support (similar to what Pilvo did/is doing). TwiML is a markup language for Twilio, which can be used to control what happens on connected calls. Continuing with the blunt approach, SignalWire calls this LāML – Legacy Antiquated Markup Language.

While this may fit a certain type of Twilio customers, it certainly doesn’t cover the whole gamut of Twilio services today.

Flexibility

On the flexibility front, there’s mostly marketing messages today and not any real announced products on the SignalWire website.

Besides LāML there’s a WebSocket based client API/SDK, not so different than what you’ll find elsewhere.

They can probably get away with it in the sales process by saying “we give you FreeSWITCH from the source”, but I am not sure what happens when developers want to configure that elastic cloud service the way they are used to be doing with their own FreeSWITCH installation.

All in all, this is an interesting offering and an interesting approach and go to market.

TeleSign – Security and Data Analytics

TeleSign is focused on SMS. And a bit of voice. As their website states: “APIs Delivering User Verification, Data Insights & Communications”

Since security, verification and fraud prevention these days rely heavily on analytics, TeleSign are “horeding” data about phone numbers, using it for these use cases. It isn’t that others don’t do it (there’s Twilio Authy, nexmo Number Insight and others), but this is what they are putting front and center.

Since their acquisition by BICS, a wholesale operator for wireline and wireless carries, that has grown even further, as they gain access to more and more data.

It will be interesting to see how TeleSign grows their business from security to additional communication domains, or will they try to focus on security and expand from the telecom space to adjacent areas.

Twilio – Adjacencies

Talking about adjacencies, that’s what Twilio is doing. Now that they are a public company, there is even more insatiability for growth within Twilio, in an effort to find more revenue streams. So far, this has worked great for Twilio.

Here are two areas we’ve seen Twilio going into:

  1. Contact centers, shifting away from developers per se with their CPaaS platform towards a cloud based contact center offering, competing head to head with some of their own customers (that would be Twilio Flex)
  2. Email, through the acquisition of SendGrid

How email fits into the Twilio communication APIs is still an open question, though I can see a few interesting initiatives there.

And then there’s the wireless offering of Twilio, which resembles a more flexible M2M play.

But where would Twilio go next?

UCaaS, going after unified communications vendors and competing with them head to head?

Maybe try to jump towards an Intercom like service of its own? Or purchase Intercom?

Or find another market of developers that is growing nicely – similar maybe to its recent Stripe integration of Twilio Pay.

Twilio in a way has been defining and redefining what CPaaS is for the past several years. They need to continue doing that to stay in the lead and well ahead of their competition.

VoIP Innovations – Marketplace

VoIP Innovations came out with what they call Showroom.

Here’s a short video of the explanation of what that is exactly:

Many of the CPaaS vendors offer a partner program of sorts. This is where vendors who develop stuff for others or build tooling and apps on top of the CPaaS vendor’s APIs can go and showcase their work. The programs vary from CPaaS company to another.

Twilio has Showcase as well as an add-on marketplace of sorts. Nexmo has a partners directory. VoIP Innovations are banking on their showroom.

What makes it different a bit is the target audience associated with it:

  1. Developers – obvious, as CPaaS caters first and foremost for developers
  2. Resellers – who can pick off marketplace apps, whitelabel and resell them
  3. Subscribers – who pay for that privilege

While there isn’t much documentation to go about, I am assuming that the whole intent behind the marketplace is to offer direct monetization opportunities for developers and resellers by taking care of customer acquisition as well as payment on behalf of the developer and reseller.

A concept taken from other marketplaces (think mobile app stores). It will be intersting to see how successful this will be.

Vonage – UCaaS+CPaaS

Vonage is interesting. Started as consumer VoIP, turned cloud UC vendor (=enterprise communications) through acquisitions, turned to acquire Nexmo and then TokBox to add CPaaS, continued with NewVoiceMedia acquisition to cover contact center space.

How does one differentiate in such a way? Probably by leveraging synergies across its product offerings and markets.

What Vonage recently did was bring number programmability from its Nexmo/CPaaS offering to its VBC/UCaaS platform.

What do they gain?

  1. Single API across product lines, making it easier to learn and use the same APIs
  2. Large ecosystem of developers using Nexmo able to build on VBC – it is… the same API
  3. The level of flexibility that a CPaaS platform has right on top of a UCaaS offering. In this case, scripting using Nexmo NCCO

Is this good for Nexmo customers and partners? Yap. They can now reach out to the Vonage business customers as an additional target market.

Is this good for Vonage customers and partners? Yap. They can now do more, and more customized communications solutions with this added flexibility.

Voximplant – Flow

Voximplant is one of the lesser known CPaaS vendors. Its whole platform is built on the concept of an App Engine, where you write the communications logic right onto their platform using Java Script. It is serverless from the ground up. A year or two ago, Voximplant added Smartcalls. A product that enables you to sketch out call flows for outbound interactions – marketing, sales, etc. These interactions would be played out across a large number of phone numbers and get automated, making it really easy and flexible to drive phone based campaigns.

Now? Voximplant took the next step of adding inbound interactions, covering the IVR and contact center types of scenarios.

Twilio, MessageBird and Plivo offer inbound visual flow products. These allow developers to drag and drop communication widgets to build a flow – a customer interaction through the system.

Voximplant and Infobip offer inbound and outbound flows, where you can also plot company/agent based initiatives with greater ease as well as the customer initiated interactions.

Why aren’t you listed here?

The CPaaS market is large and varied. It is hard to see everyone all the time. It is also hard to innovate and differentiate every year. The vendors here are the ones I had briefings with or ones who promoted their products in ways that were visible to me. But more than anything, these are the ones that I felt have changed their offerings in the past year in a differentiating manner.

BTW – if you think that differentiation here means that it is a functionality that other vendors don’t have then you are wrong. Doing that is close to impossible today. Differentiation is simply where each vendor is putting his focus and trying to attract customers and carve his niche within the broader market. It is the stories each vendor tells about his product.

If you feel like a vendor needs to be here, or did something meaningful and interesting, just contact me. I am always happy to learn more about what is happening in the market.

Who is missing in my WebRTC PaaS report?

Later this month, I will be releasing my latest update of the WebRTC PaaS report.

There are changes taking place in the market, and what vendors are offering in the WebRTC space as a managed API service is also changing. This report is there to guide buyers and sellers in the market on what to do.

For buyers, it is about which platform to pick for their project – or in some cases, in which of the platform vendors to invest.

For sellers, it is about what to add to their roadmap. To understand how they are viewed from the outside and how do they compare to their peers.

Here’s who’s been in the last update of the report:

Think you should be there? Contact me.

Want to purchase the report? There’s a 30% discount on it from today and until the update gets published (and yes – you will be receiving the update once it gets published for no additional fee).

There will be a new appendix in the report, covering the topic of Flow and Embeddable trends in the market. Something that will become more important as we move forward.

The post CPaaS differentiation in 2019 appeared first on BlogGeek.me.

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