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.
5G is in key focus as we approach IBC and few are more invested in it than BT/EE in the UK. TVB Europe gives the platform to Matt Stagg from BT to explain what 5G means to them.
Date: 22nd August, 15:00 BST
Topics will include:
– How can 5G be used for remote production?
– What does network slicing mean for production process?
– What impact will 5G have on traditional pay-TV? Will it help operators find a bigger audience as they fight against the streaming services?
– Will 5G see consumers become more interested in virtual reality?
– Could 5G see the death of broadband?
– How far away is 6G?
AV1 has strong backing from tech giants but is still seldom seen in the wild. Find out what the plans are for the future with Google’s Debargha Mukherjee.
Debargha’s intent in this talk is simple: to frame a description of what AV1 can do and is doing today in terms of the history of the codec and looking forward to the future and a potential AV2.
The talk starts by demonstrating the need for better video codecs not least of which is the statistic that by 2021, 81% of the internet’s traffic is expected to be video. But on top of that, there is a frustration with the slow decade-long refresh process which is traditional for video codecs. In order to match the new internet landscape with fast-evolving services, it seemed appropriate to have a codec which not only delivered better encoding but also saw a quicker five-year refresh cycle.
As a comparison to the royalty-free AV1, Debargha then looks at VP9 it is deployed. Further more, VP10 who’s development was stopped and diverted into the AV1 effort which is then the topic for the next part of the talk; the Alliance for Open Media, the standardisation process and then a look at some of the encoding tools available to archive the stated aims.
To round off the description of what’s presently happening with AV1 trials of VP9, HEVC and AV1 are shown demonstrating AV1s ability to improve compression for a certain quality. Bitmovin and Facebook’s tests are also highlighted along with speed tests.
Looking, now, to the future, the talk finishes by explaining the future roadmap for hardware decoding and other expected milestones in the coming years plus the software work such as SVT-AV1 and DAV1D for optimised encoding and decoding. With the promised five-year cycle, we need to look forward now to AV2 and Debargha discusses what it might be and what it would need to achieve.
Per-title encoding with machine learning is the topic of thie video from MUX.
Nick Chadwick explains that rather than using the same set of parameters to encode every video, the smart money is to find the best balance of bitrate and resolution for each video. By analysing a large number of combinations of bitrate and resolution, Nick shows you can build what he calls a ‘convex hull’ when graphing against quality. This allows you to find the optimal settings.
Doing this en mass is difficult, and Nick spends some time looking at the different ways of implementing it. In the end, Nick and data scientist Ben Dodson built a system which optimses bitrate for each title using neural nets trained on data sets. This resulted in 84% of videos looking better using this method rather than a static ladder.