Today, Facebook deals with its high demand for encoding high-quality video content by combining a benefit-cost model with a machine learning (ML) model that lets us prioritize advanced encoding for highly watched videos. Which means there needs to be a way to prioritize which videos need to be encoded using more advanced codecs.
From a pure computing perspective, applying the most advanced codecs to every video uploaded to Facebook would be prohibitively inefficient. But while more advanced codecs like VP9 provide better compression performance over older codecs, like H264, they also consume more computing power. To help with this, we employ a variety of codecs as well as adaptive bitrate streaming ( ABR ), which improves the viewing experience and reduces buffering by choosing the best quality based on a viewer’s network bandwidth.
But the sheer volume of video content on Facebook also means finding ways to do this that are efficient and don’t consume a ton of computing power and resources.
Making sure every video is delivered at the best quality - with the highest resolution and as little buffering as possible - means optimizing not only when and how our video codecs compress and decompress videos for viewing, but also which codecs are used for which videos. People upload hundreds of millions of videos to Facebook every day.
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