Stanford Researchers Launch Free TV Service to Improve Live Streaming Using AI
thestreamable.com
thestreamable.com
We're working on a paper for submission to a research conference soon, so every viewer gives us helpful data for the system to learn in-situ and improve its performance over different kinds of Internet connections and congestion situations. (We are randomizing connections to get either our in-development algorithm or a bunch of competing ABR and congestion-control schemes so we can properly evaluate, so if you get a stall or bad quality...... we'll blame that.)
All the source code is at https://github.com/StanfordSNR/puffer and is or will be released with an open-source license. The study is led by my doctoral student Francis Yan, along with Sadjad Fouladi, Hudson Ayers, Chenzhi Zhu, and my colleague Philip Levis. The goal is to train video-streaming algorithms (bitrate selection and congestion control) online, continually, in situ, to minimize stalls and deliver the best picture quality across a diverse range of real-world Internet connections and users. This is from some of the same people who brought you Salsify (functional video compression for lower-latency real-time video: https://snr.stanford.edu/salsify), Lepton (distributed/parallel JPEG compression: https://github.com/dropbox/lepton), Mosh (functional terminal emulation for mobility: https://mosh.org), Remy (machine learning for congestion control; http://mit.edu/remy), and the Pantheon of Congestion Control (https://pantheon.stanford.edu).
There are some unusual details of the Puffer system to make experimentation easier (and, we hope, performance better): the ABR algorithm is server-side, and video is streamed continuously over a WebSocket and the client gives asynchronous feedback, instead of using DASH HTTP request/replies. The tcp_info struct (including the delivery rate estimate) from the congestion-control layer is plumbed through to the ABR, and to the neural network that tries to predict "how long will it take to send a video chunk of length y," so it's weakly cross-layer. The "transmission-time predictor" is probabilistic instead of simply producing a point estimate. We encode with libx264 and then measure the SSIM of every encoded chunk to calculate a reward for each possible stream (instead of making the assumption that more bitrate is better -- the correlation is not so good when you consider different times in the same video). And we encode 10 variants of each channel, so more than typical. And everything is done at 60 fps. Of course some of these details mean these algorithms are not going to be deployed any time soon on a production CDN or streaming service, but the hope is to demonstrate the value of some of the ideas in an academic setting for future commercial deployment. The use of WebSockets, MSE, and Opus is why this doesn't work on Safari or on iOS.
FAQ here: https://puffer.stanford.edu/faq/
Also I'm happy we were able to make some of the Grafana monitoring public: https://puffer.stanford.edu/monitoring
Also, the monitoring doesn't appear to be very public.
This part of this project alone is good enough to improve, dare I claim disrupt, enormous monied interests in the global media landscape.
If this whole thing is legal (500 simultaneous peers), and can be reduced to a docker image and some rabbit ears, you could probably improve the lives of millions (60?) of Americans that do not have access to local stations.
Also, the claim of "not going to be deployed any time soon on a production CDN or streaming service", I would like to make a friendly wager that you are mistaken. I won't even demand favorable odds because of my disadvantaged intellect. This is impressive and valuable work.
My applause.
Puffer team has done a great job.
As a sidenote, I just realized how annoying cable TV ads are. The content to advertising ratio on cable is insanely bad. This 'Puffer' experience just validated my decision to stay away from cable TV.
I think what makes it so smooth is something quite simple. Its letting the previous stream run while it buffers the new one and switches you over?
It doesn't seem significantly different to me than the aereo tv situation.
Very cool project!
See about VCast for example. https://www.nextinpact.com/news/105549-lextension-copie-priv...
Note, you still have to deal with TV ads and there is no scrubbing. I wonder if you could make your own client to this and get DVR functionality? Actually since they are just grabbing over-the-air signals, I wonder if there is a SDR project for this.