Bitmovin

Video: Futuristic Codecs and a Healthy Obsession with Video Startup Time

Posted on by Russell Trafford-Jones

These next 12 months are going to see 3 new MPEG standards being released. What does this mean for the industry? How useful will they be and when can we start using them? MPEG’s coming to the market with a range of commercial models to show it’s learning from the mistakes of the past so it should be interesting to see the adoption levels in the year after their release. This is part of the second session of the Vienna Video Tech Meetup and delves into startup time for streaming services.

In the first talk, Dr. Christian Feldmann explains the current codec landscape highlighting the ubiquitous AVC (H.264), UHD’s friend, HEVC (H.265), and the newer VP9 & AV1. The latter two differentiate themselves by being free to used and are open, particularly AV1. Whilst slow, both the latter are seeing increasing adoption in streaming, but no one’s suggesting that AVC isn’t still the go-to codec for most online streaming.

Christian then introduces the three new codecs, EVC (Essential Video Coding), LCEVC (Low-Complexity Enhancement Video Coding) and VVC (Versatile Video Coding) all of which have different aims. We start by looking at EVC whose aim is too replicate the encoding efficiency of HEVC, but importantly to produce a royalty-free baseline profile as well as a main profile which improves efficiency further but with royalties. This will be the first time that you’ve been able to use an MPEG codec in this way to eliminate your liability for royalty payments. There is further protection in that if any of the tools is found to have patent problems, it can be individually turned off, the idea being that companies can have more confidence in deploying the new technology.

The next codec in the spotlight is LCEVC which uses an enhancement technique to encode video. The aim of this codec is to enable lower-end hardware to access high resolutions and/or lower bitrates. This can be useful in set-top boxes and for online streaming, but also for non-broadcast applications like small embedded recorders. It can achieve a light improvement in compression over HEVC, but it’s well known that HEVC is very computationally heavy.

LCEVC reduces computational needs by only encoding a lower resolution version (say, SD) of the video in a codec of your choice, whether that be AVC, HEVC or otherwise. The decoder will then decode this and upscale the video back to the original resolution, HD in this example. This would look soft, normally, but LCEVC also sends enhancement data to add back in the edges and detail that would have otherwise been lost. This can be done in CPU whilst the other decoding could be done by the dedicated AVC/HEVC hardware and naturally encoding/decoding a quarter-resolution image is much easier than the full resolution.

Lastly, VVC goes under the spotlight. This is the direct successor to HEVC and is also known as H.266. VVC naturally has the aim of improving compression over HEVC by the traditional 50% target but also has important optimisations for more types of content such as 360 degree video and screen content such as video games.

To finish this first Vienna Video Tech Meetup, Christoph Prager lays out the reasons he thinks that everyone involved in online streaming should obsess about Video Startup Time. After defining that he means the time between pressing play and seeing the first frame of video. The longer that delay, the assumption is that the longer the wait, the more users won’t bother watching. To understand what video streaming should be like, he examines Spotify’s example who have always had the goal of bringing the audio start time down to 200ms. Christophe points to this podcast for more details on what Spotify has done to optimise this metric which includes activating GUI elements before, strictly speaking, they can do anything because the audio still hasn’t loaded. This, however, has an impact of immediacy with perception being half the battle.

“for every additional second of startup delay, an additional 5.8% of your viewership leaves”

Christophe also draws on Akamai’s 2012 white paper which, among other things, investigated how startup time puts viewers off. Christophe also cites research from Snap who found that within 2 seconds, the entirety of the audience for that video would have gone. Snap, of course, to specialise in very short videos, but taken with the right caveats, this could indicate that Akamai’s numbers, if the research was repeated today, may be higher for 2020. Christophe finishes up by looking at the individual components which go towards adding latency to the user experience: Player startup time, DRM load time, Ad load time, Ad tag load time.

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Speakers

Christian Feldmann Dr. Christian Feldmann
Team Lead Encoding,
Bitmovin
Christoph Prager Christoph Prager
Product Manager, Analytics
Bitmovin
Markus Hafellner Markus Hafellner
Product Manager, Encoding
Bitmovin

Video: All you need to know about video KPIs

Posted on by Russell Trafford-Jones

KPIs are under the microscope as Milan’s Video Tech meet up fights against the pandemic by having its second event online and focused on measuring, and therefore improving, streaming services.

Looking at ‘Data-Driven Business Decision Making‘, Federico Preli, kicks off the event looking at how to harness user data to improve the user experience. He explains this using Netflix’s House of Cards as an example. Netflix commissioned 2 seasons of House of Cards based not on a pilot, but on data they already have. They knew the British version had been a hit on the platform, they could see that the people who enjoyed that, also watched other films from Kevin Spacey or David Fincher (the director of House of Cards). As such, this large body of data showed that, though success was not guaranteed, there was good cause to expect people to be receptive to this new programme.

Federico goes on to explain how to balance recommendations based upon user data. A balance is necessary, he explains, to avoid a bubble around a viewer where the same things keep on getting recommended and not to exaggerate someone’s interests at the detriment of nuance and not representing the less prominent predilections. He outlines the 5 parts of a balanced recommendations experience: Serendipity, diversity, coverage, fairness & trust. Balancing these equally will provide a rounded experience. Finally, Federico discusses how some platforms may choose to under invest in some of these due to the nature of their platforms. Relevance, for instance, may be less important for an ultra-niche platform where everything has relevance.

‘Performance Video KPIs at the Edge‘ is the topic of Luca Moglia‘s talk. A media solutions engineer from Akamai, he looks at how to derive more KPI information from logs at the edge. Whilst much data comes from a client-side KPI, data directly reported by the video player itself to the service. Client-side information is vital as only the client knows on which button you clicked, for instance and how long you spent in certain parts of the GUI. But in terms of video playback, there is a lot to be understood by looking at the edge, the part of the CDN which is closest to the client.

One aspect that client-side reporting doesn’t cover is use of the platform by clients which aren’t fully supported meaning they report back less information. Alternatively, for some services, it may be possible to access them with clients which don’t report at all. Depending on how reporting is done, this could be blocked by ad blockers or DNS rules. As such, this is an important gap which can be largely filled by analysis of CDN logs. This allows you to enhance the data analysis done elsewhere and validate it.

Luca gives examples of KPIs that can be measured or inferred from the edge, such as ‘hand-waving latency’ which can be understood from the edge-to-origin latency and time to manifest. He also shows an example graph analysing the number of segments served at the edge within the segment duration time. This helps indicate how many streams weren’t rebuffering. Overall, Luca concludes, analysing data from the edge helps track improvements, gives you better visibility on consumer/global events and allows you to enhance the performance of the platform.

Bitmovin’s Andrea Fassina covers ‘Client KPIs – Five Analytics Metrics That Matter‘ which he summarises at the beginning of his talk ahead of explaining each individually. ‘Impressions & Total Hours Watched’ is first. This metric has really shown its importance as the SARS-CoV-2 pandemic has rolled around the globe. Understanding how much more people are watching is important in understanding how your platform is reacting. After all, if a platform is struggling this could be for many reasons that are correlated with, but not because of, more hours streamed. For instance, in boxing matches, it’s often the payment system which struggles before the streaming does.

Video startup time is next. Andrea explains the statistics of lost viewers as your time-to-play increases. You can look at startup time across each device and see where the low-hanging fruit for improvements and prioritise your work. This metric can be extended to ad playing and DRM load time which need to be brought into the overall equation.

Third is Video Bitrate Heatmap which allows you to see which type of chunks are most used and, similarly, which rungs on your ABR ladder aren’t needed (or could be improved.) The fourth KPI discussed is Error Types and Codes. Analysing codes generated can give you early warning to issues and allow you to understand whether you suffer more problems than the industry average (6.6%) but also proactively talk to connectivity providers to reduce problems. Lastly, Andrea explains how Rebuffering percentage helps understand where there are gaps in your service in terms of devices/apps which are particularly struggling.

Source: Andrea Fassina, Bitmovin

‘Video Quality Metrics‘ rounds off the session as Fabio Sonnati tackles the tricky problem of how to know what quality of video each viewer is seeing. Given that the publisher has each and every chunk and can view them, many would think this would mean you could see exactly what each stream would look like. But a streaming service can only see what each chunk looks like on their device in their environment. When you view a chunk encoded at 1080i on an underpowered SD device, what does the user actually see and would they have been better receiving a lower resolution, lower bitrate chunk instead?

In order to understand video quality, Fabio briefly explains some objective metrics such as VMAD, SSIM and PSNR. He then discusses the way that Sky Italia have chosen to create their own metric by combining metrics, subjective feedback and model training. The motivation to do this, to tailor your metric to the unique issues that your platform has to contend with. This metric, called SynthEYE, has been expanded to be able to run without a reference – i.e. it doesn’t require the source as well as the encoded version. Fabio shows results of how well SynthEYE Absolute predicts VMAF and MOS scores. He concludes by saying that using an absolute metric is useful because it gives you the ability to analyse chunk-by-chunk and then match that up with resolution and other analytics data to better understand the performance of the platform.

The session concluded with 20 minutes of Q&A

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Speakers

Luca Moglia Luca Moglia
Media Solutions Engineer,
Akamai
Andrea Fassina Andrea Fassina
Developer Evangelist,
Bitmovin
Fabio Sonnati Fabio Sonnati
Media Architect and
Encoding & Streaming Specialist
Federico Preli Federico Preli
Senior Solution Architect,
ContentWise
Stefano Morello Moderator: Stefano Morello
Senior Sales Engineer,
ContentWise

Video: OTT Fundamentals & hands-on video player lab

Posted on by Russell Trafford-Jones

Whilst there are plenty of videos explaining the basics streaming, few of them talk you through the basics of actually implementing a video player on your website. The principles taught in this hands-on Bitmovin webinar are transferable to many players, but importantly at the end of this talk you’ll have your own implementation of a video player which you can make in real time using their remix project at glitch.com which allows you to edit code and run it immediately in the browser to see your changes.

Ahead of the tutorial, the talk both explains the basics of compression and OTT led by Kieran Farr, Bitmovin’s VP of marketing and Andrea Fassina, Developer Evangelist. Andrea outlines a simplified OTT architecture where he looks at the ‘ingest’ stage which, in this example, is getting the videos from Instagram either via the API or manually. It then looks at the encoding step which compresses the input further and creates a range of different bitrates. Andrea explains that MPEG standards such as H.264, H.265 are commonly used to do this making the point that MPEG standards typically require royalty payments. This year, we are expecting to see VVC released by MPEG (H.266).

Andrea then explains the relationship between resolution, frame rate and file sizes. Clearly smaller files are better as they require less time to download leading to quicker downloads so faster startup times. Andrea discusses how the resolutions match the display resolutions with TVs having 1920×1080 resolution or 2160×3840 resolution. Given that higher resolutions have more picture detail, there is more information to be sent leading to larger file sizes.

Source: Bitmovin https://bit.ly/2VwStwC

When you come to set up your transcoder and player, there are a number of options you need to set. These are determined by these basics, so before launching into the code, Andrea looks further into the fundamental concepts. He next looks at video compression to explain the ways in which compression is achieved and the compromises within. Andrea starts from the first MJPEG codecs where each frame was its own JPEG image and they simply animated from one JPEG to another to show the video – not unlike animated GIFs used on the internet. However by treating each frame on its own ignores a lot of compression opportunity. When looking at one frame to the next, there are a lot of parts of the image which are the same or very similar. This allowed MPEG to step up their efforts and look across a number of frames to spot the similarities. This is typically referred to as temporal compression as is it uses time as part of the process.

In order to achieve this, MPEG splits all frames into blocks, squares in AVC, which are called macro blocks which be compared between frames. They then have 3 types of frame called ‘I’, ‘P’ and ‘B’ frames. The I frames have a complete description of that frame, similar to a JPEG photograph. P frames don’t have a complete description of the frame, rather they some blocks which have new information and some information saying that ‘this block is the same as this block in this other frame. B frames have no complete new image parts, but create the frame purely out of frames from the recent future and recent past; the B stands for ‘bi-directional’.

Ahead of launching into the code, we then look at the different video codecs available. He talks about AVC (discussed in detail here), HEVC (detailed in this talk) and compares the two. One difference is HEVC uses much more flexible macro block sizes. Whilst this increases computational complexity, it reduces the need to send redundant information so is an important part of the achieving the 50% bitrate reduction that HEVC typically shows over AVC. VP9 and AV1 complete the line-up as Andrea gives an overview of which platforms can support these different codecs.

Source: Bitmovin https://bit.ly/2VwStwC

Andrea then introduces the topic of Adaptive bitrate, ABR. This is vital in the effective delivery of video to the home or mobile phones where bandwidth varies over time. It requires creating several different renditions of your content at various bitrates, resolutions and even frame rate. Whilst these multiple encodes put a computational burden on the transcode stage, it’s not acceptable to allow a viewer’s player to go black, so it’s important to keep the low bitrate version. However there is a lot of work which can go into optimising the number and range of bitrates you choose.

Lastly we look at container formats such as MP4 used in both HLS and MPEG-DASH and is based on the file format ISO BMFF. Streaming MP4 is usually called fragmented MP4 (fMP4) as it is split up into chunks. Similarly MPEG2 Transport Streams (TS files) can be used as a wrapper around video and audio codecs. Andrea explains how the TS file is built up and the video, audio and other data such as captions are multiplexed together.

The last half of the video is the hands-on section during which Andrea talks us through how to implement a video player in realtime on the glitch project allowing you to follow along and do the same edits, seeing the results in your browser as you go. He explains how to create a list of source files, get the player working and styled correctly.

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Download the presentation
Speakers

Kieran Farr Kieran Farr
VP of Marketing,
Bitmovin
Andrea Fassina Andrea Fassina
Developer Evangelist,
Bitmovin

Video: Tech Talks: Low-Latency Live Streaming

Posted on by Russell Trafford-Jones

There are a number of techniques for achieving low-latency streaming. This talk is one of the few which introduces them in easy to understand ways and then puts them in context briefly showing the manifests or javascript examples of how these would be seen in the wild. Whilst there are plenty of companies who don’t need low-latency streaming, for many it’s a key part of their offering or it’s part of the business model itself. Knowing the techniques in play is to better understand internet streaming in general.

Jameson Steiner from Bitmovin starts by explaining why there is a motivation to cut the latency. One big motivation, aside from the standard live sports examples, is user-generated content like on Twitch where it’s very clear to the streamer, and quite off-putting, when there is large amounts of delay. Whilst delay can be adapted to, the more there is the less interaction is possible. In this situation, it’s the ‘handwaving’ latency that comes in to play. You want the hand on the screen to wave pretty much at the same time as your hand waves in front of the camera. Jameson places different types of distribution on a chart showing latency and we see that low-latency of 5 seconds or less will not only match traditional TV broadcasts, but also work well for live streamers.

Naturally, to fix a problem you need to understand the problem, so Jameson breaks down the legacy methods of delivery to show why the latency exists. The issue comes down to how video is split into sections, say 6 seconds, so that the player downloads a section at a time, reassembles and plays them. Looking from the player’s perspective, if the network suddenly broke or reduced its throughput, it makes sense to have several chunks in reserve. Having three 6-second chunks, a sensible precaution, makes you 18 seconds behind the curve from the off.

Clearly reducing the segement size is a winner in this scenario. Three 3 second segments will give you just 9 seconds latency; why not go to 1 second? Well encoding inefficiency is one reason. If you reduce the amount of time a temporal codec has of a video, its efficiency will drop and bitrate will increase to maintain quality. Jameson explains the other knock-on effects such as CDN inefficiencies and network requests. The standardised way to avoid these problems is to use CMAF (Common Media Application Format) which is based on MPEG DASH and ISO BMFF. CMAF, and DASH in general, has the benefit of coming from a standards body whose aim was to remove vendor lock-in that may be felt with HLS and was certainly felt with RTMP. Check out MPEG’s short white paper on the topic (zipped .docx file)

CMAF uses chunked transfer meaning that as the encoder writes the data to the disk, the web server sends it to the client. This is different to the default where a file is only sent after it’s been completely written. This has the effect of the not having to wait up to 6 seconds to a 6-second chunk to start being sent; the download time also needs to be counted. Rather, almost as soon as the chunk has been finished by the encoder, it’s arrived at the destination. This is a feature of HTTP 1.1 and after so is not new, but it still needs to be enabled and considered as part of the delivery.

CMAF goes beyond simple HTTP 1.1 chunked transfer which is a technique used in low-latency HLS, covered later, by creating extra structure within the 6-second segment (until now, called a chunk in this article). This extra structure allows the segment to be downloaded in smaller chunks decoupling the segment length from the player latency. Chunked transfer does cause a notable problem however which has not yet been conclusively solved. Jameson explains how traditionally each large segment typically arrives faster than realtime. By measuring how fast it arrives, given the player knows the duration, it can estimate the bandwidth available at that time on the network. With chunked transfer, as we saw, we are receiving data as it’s being created. By definition, we are now getting it in realtime so there is no opportunity to receive it any quicker. The bandwidth estimation element, as shown the presentation, is used to work out if the player needs to go down or could go up to another stream at a different bitrate – part of standard ABR. So the catastrophe here is the going down in latency has hampered our ability to switch bitrates and whilst the viewer can see the video close to real-time, who’s to say if they are seeing it at the best quality?

Low-Latency HLS/DASH is a way of extending DASH and HLS without using CMAF. Jameson explains some techniques such as advertising segments in advance to allow players to pre-request. It also relies on finding the compromise point of encoding inefficiency vs segment length, typically held to be around 2 seconds, to minimise the latency. At this point we start seeing examples of the techniques in manifests and javascript allowing us to understand how this is actually signalled and implemented.

Apple is on its second major revision of LL-HLS which has responded to many of the initial complaints from the community. Whilst it can use HTTP/2 to help push segments out, this caused problems in practice so it can now preload hints, as Jameson explains in order to remove round-trip times from requests. Jameson looks at the other of Apple’s techniques and shows how they look in manifest files.

The final section looks at problems in implementing these features such as chunks being fragmented across TCP packets, the bandwidth estimation question and dealing with playback speed in order to adjust the players position in time – speed-ups and slow-downs of 5 to 10% can be possible depending on content.

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Download the presentation
Speaker

Jameson Steiner Jameson Steiner
Software Engineer,
Bitmovin