How can we overcome one of the last, big, problems in making CMAF a generally available: making ABR work properly.
ABR, Adaptive Bitrate is a technique which allows a video player to choose what bitrate video to download from a menu of several options. Typically, the highest bitrate will have the highest quality and/or resolution, with the smallest files being low resolution.
The reason a player needs to have the flexibility to choose the bitrate of the video is mainly due to changing network conditions. If someone else on your network starts watching some video, this may mean you can no longer download video quick enough to keep watching in full quality HD and you may need to switch down. If they stop, then you want your player to switch up again to make the most of the bitrate available.
Traditionally this is done fairly simply by measuring how long each chunk of the video takes to download. Simply put, if you download a file, it will come to you as quickly as it can. So measuring how long each video chunk takes to get to you gives you an idea of how much bandwidth is available; if it arrives very slowly, you know you are close to running out of bandwidth. But in low-latency streaming, your are receiving video as quickly as it is produced so it’s very hard to see any difference in download times and this breaks the ABR estimation.
He starts by explaining how players currently behave with low-latency ABR showing how they miss out on changing to higher/lower renditions. Then he looks at the differences on the server and for the player between non-low-latency and low-latency streams. This lays the foundation to discuss ACTE – ABR for Chunked Transfer Encoding.
ACTE is a method of analysing bandwidth with the assumption that some chunks will be delivered as fast as the network allows and some won’t be. The trick is detecting which chunks actually show the network speed and Ali explains how this is done and shows the results of their evaluation.
Many companies would love to be using free codecs, unencumbered by patents, rather than paying for HEVC or AVC. Phil Cluff shows that, contrary to popular belief, it is possible stream with free codecs and get good coverage on mobile and desktop.
Phil starts off by looking at the codecs available and whether they’re patent encumbered with an eye to how much of the market can actually decode them. Free codecs and containers like WebM, VP8 etc. are not supported by Safari which reduces mobile penetration by half. To prove the point, Phil presents the results of his trials in using HEVC, AVC and VP8 on all major browsers.
Whilst this initially leaves a disappointing result for streaming with libre codecs on mobile, there is a solution! Phil explains how an idea from several years ago is being reworked to provide a free streaming protocol MPAG-SASH which avoids using DASH which is itself based on ISO BMFF which is patent encumbered. He then explains how open video players like video.js can be modified to decode libre codecs.
With these two enhancements, we finally see that coverage of up to 80% on mobile is, in principle, possible.
Netflix’s Anne Aaron explains how VMAF came about and how AV1 is going to benefit both the business and the viewers.
VMAF is a method for computers to calculate the quality of a video in a way which would match a human’s opinion. Standing for Video Multi-Method Assessment Fusion, Anne explains that it’s a combination (fusion) of more than one metric each harnessing different aspects. She presents data showing the increased correlation between VMAF and real-life tests.
Anne’s job is to maximise enjoyment of content through efficient use of bandwidth. She explains there are many places with wireless data is limited so getting the maximum amount of video through that bandwidth cap is an essential part of Netflix’s business health.
This ties in with why Netflix is part of the Alliance for Open Media who are in the process of specifying AV1, the new video codec which promises bitrate improvements over-and-above HEVC. Anne expands on this and presents the aim to deliver 32 hours of video using AV1 for 4Gb subscribers.
Honing the use of AI and Machine Learning continues apace. Streaming services are particularly ripe areas for AI, but the winners will be those that have managed to differentiate themselves and innovate in their use of it.
Artificial Intelligence (AI) and Machine Learning (ML) are related technologies which deal with replicating ‘human’ ways of recognising patterns and seeking patterns in large data sets to help deal with similar data in the future. It does this without using traditional methods like using a ‘database’. For the consumer, it doesn’t actually matter whether they’re benefitting from AI or ML, they’re simply looking for better recommendations, wanting better search and accurate subtitles (captions) on all their videos. If these happened because of humans behind the scenes, it would all be the same. But for the streaming provider, everything has a cost, and there just isn’t the ability to afford people to do these tasks plus, in some cases, humans simply couldn’t do the job. This is why AI is here to stay.
Date: Thursday 8th August, 16:00 BST / 11am EDT
In this webinar from IBC365, Media Distillery, Liberty Global and Grey Media come together to discuss the benefits of extracting images, metadata and other context from video, analysis of videos for contextual advertising, content-based search & recommendations and ways to maintain younger viewers.
AI will be here to stay touching the whole breadth of our lives, not just in broadcast. So it’s worth learning how it can be best used to produce television, for streaming and in your business.