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If you’re new to WebRTC, Jitsi was the first open source Selective Forwarding Unit (SFU) and continues to be one of the most popular WebRTC platforms. They were in the news last week because their parent group inside Atlassian was sold off to Slack but the team clarified this does not have any impact on the Jitsi team. Helping to show they are still chugging along, they released a new feature they wanted to talk about – off-stage layer suspension. This is a technique for minimizing bandwidth and CPU consumption when using simulcast. Simulcast is a common technique used in multi-party video scenarios. See Oscar Divorra’s post on this topic and that Fippo post just last week for more on that. Even if you are not implementing a  simulcast, this is a good post for understanding how to control bandwidth and to see some follow-along reverse-engineering on how Google does things in its Hangouts upgrade called Meet.

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You don’t need to fit an SFU opponent when testing simulcast. Image: Hall of Mirrors scene from Bruce Lee’s Enter the Dragon

Simulcast is one of the more interesting aspects of WebRTC for multiparty conferencing. In a nutshell, it means sending three different resolution (spatial scalability) and different frame rates (temporal scalability) at the same time. Oscar Divorra’s post contains the full details.

Usually, one needs a SFU to take advantage of simulcast. But there is a hack to make the effect visible between two browsers — or inside a single page. This is very helpful for single-page tests or fiddling with simulcast features, particular the ability to enable only certain spatial layers or to control the target bitrate of a particular stream.

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What happens when you screen share on a computer that's already sharing your screen

The Chrome Webstore has decided to stop allowing inline installation for Chrome extensions. This has quite an impact on WebRTC applications since screensharing in Chrome currently requires an extension. Will the getDisplayMedia API come to the rescue?

Screensharing in Chrome

When screensharing was introduced in Chrome 33, it required implementation via an extension as a way to address the security concerns. This was better than the previous experience of putting this capability behind a flag which lead to sites asking their users to change that flag… that got those sites an official yikes.

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Now that it is getting relatively easy to setup video calls (most of the time), we can move on to doing fun things with the video stream. With new advancements in Machine Learning (ML) and a growing number of API’s and libraries out there, computer vision is also getting  easier to do. Google’s ML Kit is a recent example of a new machine learning based library that makes gives quick access to computer vision outputs.

To show how to use Google’s new ML Kit to detect user smiles on a live WebRTC stream, I would like to welcome back past webrtcHacks author and WebRTC video master  Gustavo Garcia Bernardo of Houseparty. Joining him I would like to also welcome mobile WebRTC expert, Roberto Perez of TokBox.  They give some background on doing facial detection, show some code samples, but more importantly share their learnings for optimum configuration of smile detection inside a Real Time Communications (RTC) app.

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One of the great things about WebRTC is that it is built right into the web platform. The web platform is generally great for WebRTC, but occasionally it can cause huge headaches when specific WebRTC needs do not exactly align with more general browser usage requirements. The latest example of this is has to do with the autoplay of media where sound(s) suddenly went missing for many users. Former webrtcHacks guest author Dag-Inge Aas has been dealing with this first hand. See below for his write-up on browser expectations around the playback of media, the recent Chrome 66+ changes, and some tips and tricks for working around these issues.

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Brussel’s Mannneken Pis. Original photo by Flickr user Francisco Antunes (CC BY 2.0)

We have covered the “WebRTC is leaking your IP address” topic a few times, like when I reported what the NY Times was doing and in my WebRTC-Notifier. Periodically this topic comes up now and again in the blogosphere, generally with great shock and horror. This happened again recently, so here is an updated look into this alleged issue.

The recent blog post titled VPN Leak by voidsec highlighting how 19 out of more than 100 VPN services tested “leak” IP addresses via WebRTC is a quite interesting read. Some of the details about WebRTC are not quite correct the results are interesting nonetheless. At is core this is someone who sat down to test a long list of services and their behaviour, one by one. This is not the most exciting research task, but exhaustive studies like this often find something interesting.

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One of WebRTC’s biggest challenges has been providing consistent, reliable support across platforms. For most apps, especially those that started on the web, this generally means developing a native or hybrid mobile app in addition to supporting the web app.  Progressive Web Apps (PWA) is a new concept that promises to unify the web for many applications by allowing web-based apps to look and act like native mobile ones without introducing an intermediary hybrid framework. As will be discussed, this approach has a lot of advantages, but does it make any sense for a WebRTC app?

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I logged into YouTube on Tuesday and noticed this new camera icon in the upper right corner, with a “Go Live (New)” option, so I clicked on it to try. It turns out you can now live stream directly from the browser. This smelled a lot like WebRTC, so I loaded up chrome://webrtc-internals to see and sure enough, it was WebRTC. We are always curious here to see how large scale deployments are implemented, so I immediately asked WebRTC reverse engineering master Philipp “Fippo” Hancke to investigate deeper. The rest here is his analysis.

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In part 1 of this set, I showed how one can use UV4L with the AIY Vision Kit send the camera stream and any of the default annotations to any point on the Web with WebRTC. In this post I will build on this by showing how to send image inference data over a WebRTC dataChannel and render annotations in the browser. To do this we will use a basic Python server,  tweak some of the Vision Kit samples, and leverage the dataChannel features of UV4L.

To fully follow along you will need to have a Vision Kit and should have completed all the instructions in part 1. If you don’t have a Vision Kit, you still may get some value out of seeing how UV4L’s dataChannels can be used for easily sending data from a Raspberry Pi to your browser application.

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A couple years ago I did a TADHack  where I envisioned a cheap, low-powered camera that could run complex computer vision and stream remotely when needed. After considering what it would take to build something like this myself, I waited patiently for this tech to come. Today with Google’s new AIY Vision kit, we are pretty much there.

The AIY Vision Kit is a $45 add-on board that attaches to a Raspberry Pi Zero with a Pi 2 camera. The board includes a Vision Processing Unit (VPU) chip that runs Tensor Flow image processing graphs super efficiently. The kit comes with a bunch of examples out of the box, but to actually see what the camera see’s you need to plug the HDMI into a monitor. That’s not very useful when you want to put your battery powered kit in a remote location. And while it is nice that the rig does not require any Internet connectivity, that misses out on a lot of the fun applications. So, let’s add some WebRTC to the AIY Vision Kit to let it stream over the web.

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