Welcome to this blog

Today I'm starting this new blog to discuss trends, news and technologies around Real Time Communications.     The topics included under the Real Time Communications umbrella are those related to personal communications (voice, messaging, application sharing, presence...) using next generation networks (basically IP technologies). 

The aim of this blog is to bring a space to discover and discuss these trends, news and technologies in a simple, updated and objective way.

Suggestions for topics and any kind of comments are more than welcomed.

Comments

  1. Larry CoatesJune 26, 2017 at 6:01 AM

    Real-time use for data storage and only. Using it to backup some of my files and folders (enough 5 https://yumdownload.com/realtimes and good opportunity to save the installation files.

    ReplyDelete

Post a Comment

Popular posts from this blog

Bandwidth Estimation in WebRTC (and the new Sender Side BWE)

Image
Bandwidth estimation is probably the most critical component in the video engine of WebRTC. The bandwidth estimation (BWE) module is responsible for deciding how much video* traffic you can send without congesting the network to prevent degradation of the video quality. In the past bandwidth estimation algorithms used to be very rudimentary and based mostly on packet loss. Basically we used to start increasing slowly the video bitrate until we detected packets being lost.  To detect the packet loss you use the standard RTCP feedback mechanisms where the receiver side reports packet loss periodically using RTCP Receiver Report (RR) messages. Modern bandwidth estimation algorithms are more advanced and try to detect congestion before it is bad enough to make routers discard packets. These algorithms predicts congestion analyzing the delay between packets. The idea is that when you start having some congestion, the buffers in the routers will start filling and the delay wil...
Read more

Improving Real Time Communications with Machine Learning

Image
When we talk about the applications of Artificial Intelligence / Machine Learning (AI/ML) for Real Time Communications (RTC) we can group them in two different planes: Service Level : There are many features that can be added to a videoconference service, for example identification of the participants, augmented reality, emotion detection, speech transcription or audio translation .  These features are usually based on image and speech recognition and language processing. Infrastructure Level : There are many ways to apply ML that do not provide new features but improve the quality and/or reliability of the audio/video transmission. Service level applications are fun, but they are more for Product Managers and I like technology more, so in the next sections I will try to describe possible applications of AI/ML for Real Time Communications at Infrastructure Level organizing those ideas in five different categories. Optimizing video quality Some of the ML algorithms use...
Read more

Controlling bandwidth usage in WebRTC (and how googSuspendBelowMinBitrate works)

Image
There are cases when we would like to limit the maximum bitrate being transmitted by WebRTC to avoid wasting resources in the user endpoints or save money reducing the bandwidth usage in our servers.   This is because the maximum bitrate by default in Chrome is around 2Mbps and for many use cases a much lower bitrate provides still pretty good quality.   BTW using a lower bitrate can also help with stability of quality in multiparty scenarios by reducing the amount of competition among different streams. There is no simple API to configure the maximum bitrate in WebRTC (although there is one in ORTC) but there are 3 ways to do this by mangling the SDP. 1. U se the standard  b=AS:BITRATE (Chrome) or b=TIAS:BITRATE  (Firefox) attributes in the SDP for the audio or video channel[1] 2. Use codec specific attributes (this work at least for opus audio codec with maxaveragebitrate property) [2] 3. Use the proprietary x-max-bitrate attribute in the video channel of the...
Read more