Video: Reducing peak bandwidth for OTT

‘Flattening the curve’ isn’t just about dealing with viruses, we learn from Will Law. Rather, this is one way to deal with network congestion brought on by the rise in broadband use during the global lockdown. This and other key ways such as per-title encoding and removing the top tier are just two other which are explored in this video from Akamai and Bitmovin.

Will starts the talk explaining why congestion happens in a world where ABR (adaptive bitrate streaming) is supposed to deal with this. With Akamai’s traffic up by around 300%, it’s perhaps not a surprise there’s a contest for bandwidth. As not all traffic is a video stream, congestion will still happen when fighting with other, static, data transfers. However deeper than that, even with two ABR streams, the congestion protocol in use has a big impact as will shows with a graph showing Akamai’s FastTCP and BBR where BBR steals all the bandwidth rather than ‘playing fair’.

Using a webpage constructed for the video, Will shows us a baseline video playback and the metrics associated with it such as data transferred and bitrate which he uses to demonstrate the different benefits of bitrate production techniques. The first is covered by Bitmovin’s Sean McCarthy who explains Bitmovin’s per-title encoding technology. This approach ensures that each asset has encoder settings tuned to get the best out of the content whilst reducing bandwidth as opposed to simply setting your encoder to a fairly-high, safe, static bitrate for all content no matter how complex it is. Will shows on the demo that the bitrate reduces by over 50%.

Swapping codec, is an obvious way to reduce bandwidth. Unlike per-title encoding which is transparent to the end user, using AV1, VP9 or HEVC requires support by the final device. Whilst you could offer multiple versions of your assets to make sure you still cover all your players despite fragmentation, this has the downside of extra encoding costs and time.

Will then looks at three ways to reduce bandwidth by stopping the highest-bitrate rendition from being used. Method one is to manually modify the manifest file. Method two demonstrates how to do so using the Bitmovin player API, and method three uses the CDN itself to manipulate the manifests. The advantage of doing this in the CDN is because this allows much more flexibility as you can use geolocation rules, for example, to deliver different manifests to different locations.

The final method to reduce peak bandwidth is to use the CDN to throttle download speed of the stream chunks. This means that while you may – if you are lucky – have the ability to download at 100Mbps, the CDN only delivers 3- or 5-times the real-time bitrate. This goes a long way to smoothing out the peaks which is better for the end user’s equipment and for the CDN. Seen in isolation, this does very little, as the video bitrate and the data transferred remain the same. However delivering the video in this much more co-operative way is much more likely to cause knock-on problems for other traffic. It can, of course, be used in conjunction with the other techniques. The video concludes with a Q&A.

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Speakers

Will Law Will Law
Chief Architect,
Akamai
Sean McCarthy Sean McCarthy
Technical Product Marketing Manager,
Bitmovin

Video: Advanced Video Coding Standards AVC

Whilst the encoding landscape is shifting, AVC (AKA H.264) still dominates many areas of video distribution so, for many, understanding what’s under the hood opens up a whole realm of diagnostics and fault finding that wouldn’t be possible without. Whilst many understand that MPEG video is built around I, B and P frames, this short talk offers deeper details which helps how it behaves both when it’s working well and otherwise.

Christian Timmerer, co-founder of Bitmovin, starts his lesson on AVC with the summary of improvements in AVC over the basic MPEG 2 model people tend to learn as a foundation. Improvements such as variable block size motion compensation, multiple reference frames and improved adaptive entropy coding. We see that, as we would expect the input can use 4:2:0 or 4:2:2 chroma sub-sampling as well as full 4:4:4 representation with 16×16 macroblocks for luminance (8×8 for chroma in 4:2:0). AVC can handle Pictures split into several slices which are self-contained sequences of macroblocks. Slices themselves can then be grouped.

Intra-prediction is the next topic where by an algorithm uses the information within the slice to predict a macroblock. This prediction is then subtracted from the actual block and coded thereby reducing the amount of data that needs to be transferred. The decoder can make the same prediction and reconstruct the full block from the data provided.

The next sections talk about motion prediction and the different sizes of macroblocks. A macroblock is a fixed area on the picture which can be described by a mixture of some basic patterns but the more complex the texture in the block, the more patterns need to be combined to recreate it. By splitting up the 16×16 block, we can often find a simpler way to describe the 8×8 or 8×16 shapes than if they had to encompass a whole 16×16 block.

 

B-frames are fairly well understood by many, but even if they are unfamiliar to you, Christian explains the concept whereby B-frames provide solely motion information of macroblocks both from frames before and after. This allows macroblocks which barely change to be ‘moved around the screen’ so to speak with minimal changes other than location. Whilst P and I frames provide new macroblocks, B-frames are intended just to provide this directional information. Christian explains some of the nuances of B-frame encoding including weighted prediction.

Quantisation is one of the most important parts of the MPEG process since quantisation is the process by which information is removed and the codec becomes lossy. Thus the way this happens, and the optimisations possible are key so Christian covers the way this happens before explaining the deblocking filter available. After splitting the picture up into so many macroblocks which are independently processed, edges between the blocks can become apparent so this filter helps smooth any artefacts to make them more pleasing to the eye. Christian finishes talking about AVC by exploring entropy encoding and thinking about how AVC encoding can and can’t be improved by adding more memory and computation to the encoder.

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Speaker

Christian Timmerer Christian Timmerer
CIO & Cofounder, Bitmovin
Associate Professor, Universität Klagenfurt

Video: Let’s be hAV1ng you

AV1 is now in use for some YouTube feeds, Netflix also can deliver AV1 to Android devices so we are no longer talking about “if AV1 happens” or “when AV1’s finished”. AV1 is here to stay, but in a landscape of 3 new MPEG codecs, VVC, EVC and LCEVC, the question moves to “when is AV1 the right move?”

In this talk from Derek Buitenhuis, we delve behind the scenes of AV1 to see which AV1 terms can be, more-or-less, mapped to which MPEG terms. AV1 is promoted as a royalty free codec, although notably a patent pool has appeared to try and claim money from users. Because it’s not reusing ideas from other technologies, the names and specific functions of parts of the spec are both not identical to other codecs, but are similar in function.

Derek starts by outlining some of the terms we need to understand before delving in further such as “Temporal Unit” which of course is called a TU and is analogous to a GOP. Then he moves on to highlighting the many ways in which previous DCT-style work has been extended meaning the sizes and types of DCT have been increased, and the prediction modes have changed. All of this is possible but increases computation.

Derek then highlights several major tools which have been added. One is the prediction of the Chroma from the Luma signal. Another is the ‘Constrained Direction Enhancement Filter’ which improves the look of diagonal hard edges. The third is ‘switch frames’ which are similar to IDR frames or, as Derek puts it ‘a fancy P-frame.’ There is also a Multi-Symbolic Arithmetic Codec which is a method of guessing a future binary digit which, based on probability, allows you to encode a sub-set of the number but just enough to ensure that the algorithm will come out with the full number,

After talking about the Loop Restoration Filter Derek then critiques a BBC article which drew, it seems, incorrect conclusions based on not enabling the appropriate functions needed for good compression and also suggesting that there was not enough information provided for anyone else to replicate the experiment. Derek then finishes with MS-SIM plots of different encoders.

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Download the slides.
Speaker

Derek Buitenhuis Derek Buitenhuis
Senior Video Encoding Engineer,
Vimeo

Video: Video Compression Basics

Video compression is used everywhere we look. So often is it not practical to use uncompressed video, that everything in the consumer space video is delivered compressed so it pays to understand how this works, particularly if part of your job involves using video formats such as AVC, also known as H.264 or HEVC, AKA H.265.

Gisle Sælensminde from Vizrt takes us on this journey of creating compressed video. He starts by explaining why we need uncompressed video and then talks about containers such as MPEG-2 Transport Streams, mp4, MOV and others. He explains that the container’s job is partly to hold metadata such as the framerate, resolution and timestamps among a long list of other things.

Gisle takes some time to look at the past timeline of codecs in order to understand where we’re going from what went before. As many use the same principles, Gisle looks at the different type of frames inside most compressed formats – I, P and B frames which are used in set patterns known as GOPs – Group(s) of Pictures. A GOP defines how long is between I frames. In the talk we learn that I frames are required for a decoder to be able to tune in part way through a feed and still start seeing some pictures. This is because it’s the I frame which holds a whole picture rather than the other types o frame which don’t.

Colours are important, so Gisle looks at the way that colours are represented. Many people know about defining colours by looking at the values of Red, Green and Blue, but fewer about YUV. This is all covered in the talk so we know about conversion between the two types.

Almost synonymous with codecs such as HEVC and AVC are Macroblocks. This is the name given to the parts of the raster which have been spit up into squares, each of which will be analysed independently. We’ll look at who these macro blocks are used, but Gisle also spends some time looking to the future as both HEVC, VP9 and now AV1 use variable-size macro block analysis.

A process which happens throughout broadcast is chroma subsampling. This topic, whereby we keep more of the luminance channel than colours, is explored ahead of looking at DCTs – Discrete Cosine Transforms – which are foundational to most video codecs. We see that by analysing these macro blocks with DCTs. we can express the image in a different way and even cut down on some of the detail we get from DCTs in order to reduce the bitrate.

Before some very useful demos looking at the result of varying quantisation across a picture, the difference signal between the source and encoded picture plus deblocking technology to hide some of the artefacts which can arise from DCT-based codecs when they are pushed for bandwidth.

Gisle finishes this talk at Media City Bergen by taking a number of questions from the floor.

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Speaker

Gisle Sælensminde Gisle Sælensminde
Senior Software Engineer,
Vizrt