Video: QoE Impact from Router Buffer sizing and Active Queue Management

Netflix take to the stage at Demux to tell us about the work they’ve been doing to understand and reduce latency by looking at the queue management of their managed switches. As Tony Orme mentioned yesterday, we need buffers in IP systems to allow synchronous parts to interact. Here, we’re looking at how the core network fabric’s buffers can get in
the way of the main video flows.

Te-Yuan Huang from Netflix explains their work in investigating buffers and how best to use them. She talks about the flows that occur due to the buffer models of standard switches i.e. waiting until the buffer is full and then dropping everything else that comes in until the buffer is emptied. There is an alternative method, Active Queue Management (AQM), called FQ-CoDel which drops packets based on probability before the buffer is dropped. By carefully choosing the probability, you can actually improve buffer handling and the impact it has on latency.

Te-Yuan shows us results from tests that her team has done which show that the FQ-CoDel specification does, indeed, reduce latency. After showing us the data, she summarises saying that FQ-CoDel improves playback and QOE.

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Speaker

Te-Yuan Huang Te-Yuan Huang
Engineering Manager (Adaptive Streaming),
Netflix

Video: Buffer Sizing and Video QoE Measurements at Netflix

At a time when Netflix is cutting streaming quality to reduce bandwidth, we take a look at the work that’s gone into optimising latency within the switch at ISPs which was surprisingly high.

Bruce Spang interned at Netflix and studied the phenomenon of unexpected latency variation within the netflix caches they deploy at ISPs to reduce latency and bandwidth usage. He starts by introducing us to the TCP buffering models looking at how they work and what they are trying to achieve with the aim of identifying how big it is supposed to be. The reason this is important is that if it’s a big buffer, you may find that data takes a long time to leave the buffer when it gets full, thus adding latency to the packets as they travel through. Too small, of course, and packets have to be dropped. This creates more rebuffing which impacts the ABR choice leading to lower quality.

Bruce was part of an experiment that studied whether the buffer model in use behaved as expected and whist he found that it did most of the time, he did find that video performance varied which was undesirable. To explain this, he details the testing they did and the finding that congestion, as you would expect, increases latency more during a congested time. Moreover, he showed that a 500MB had more latency than 50MB.

To explain the unexplained behaviour such as long-tail content having lower latency than popular content, Bruce explains how he looked under the hood of the router to see how VOQs are used to create queues of traffic and how they work. Seeing the relatively simply logic behind the system, Bruce talks about the results they’ve achieved working with the vendor to improve the buffering logic.

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Speakers

Bruce Spang Bruce Spang
PhD Student, Stanford

Video: AV1 at Netflix

Netflix have continually been pushing forward video compression and analysis because their assets are played so many times that every bit saved is real money saved. VMAF is a great example of Netflix’s desire to push the state of the art forward. Developed by Netflix and two universities, this new objective metric allowed them to better evaluate the quality of videos using computer analysis and has continued to be the foundation of their work since.

One use of VMAF has been to verify the results of Netflix’s Per-Shot Encoding method which alters encoding parameters for each shot of the film rather than using a fixed set of parameters for the whole film. The Broadcast Knowledge has featured talks on their previous technique, per-title encoding (among others).

AV1, however must be the most famous innovation that Neflix is behind. A founding member of the Alliance for Open Media (AoM), Netflix saw a need a for a better codec and by making an open one, which also played to the needs of other internet giants such as Google, was a good way to create a vibrant community around it driving submissions to the codec itself but also, it is hoped, in the implementation and adoption.

In this two-part talk, LiWei Guo starts off by explaining the ways in which AV1 will be used by Netflix. Since this talk took place, Netflix has started streaming in AV1 to Android clients. LiWei points out that AV1 supports 10-bit video as standard – a notable difference from other codecs like AVC and HEVC. This allows Netflix to use 10-bit without worrying about decoder compatibility and he shows examples of skies and water which are significantly by the use of 10-bit.

Another feature of AV1 is the Film Grain synthesis which seeks to improve encoding efficiency by removing the random film grain of the source during the encode process then inserting a similar random noise on top to recreate the same look and feel. As anything random can’t be predicted, noise such of this is very wasteful for a codec to try and encode, therefore it’s not <a surprise that this can result in as much as a 30% reduction in bitrate. Before concluding, LiWei briefly explains per-shot encoding then shows data showing the overall improvements.

Andrey Norkin, also from Netflix explains their work with Intel on the SVT-AV1 software video encoder which leverages Intel’s SVT technology, a framework optimised for Xeon chips for video encoding and analysis. Netflix’s motivations are to further increase adoption by delivering a data centre-ready, optimised encoder and to create an AV1 encoder they can use to support their own internal research activities (did someone say AV2?). SVT allows for parallelisation, important for any computer nowadays with so many cores available.

Finishing up, Andrey points us to the Github repository, lets us know the development statement (as of November 2019) and looks at the speed increases that have taken off, comparing SVT-AV1 against the reference libaom encoder.

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Speakers

Andrey Norkin Andrey Norkin
Senior Research Scientist,
Netflix
LiWei Guo LiWei Guo
Senior Software Engineer,
Netflix

Video: Tidying Up (Bits on the Internet)

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.

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Speaker

Anne Aaron