With us since 1998, ABR (Adaptive Bitrate) has been allowing streaming players to select a stream appropriate for their computer and bandwidth. But in this video, we hear that over 20 years on, we’re still developing ways to understand and optimise the performance of ABRs for delivery, finding the best balance of size and quality.
Brightcove’s Yuriy Reznik takes us deep into the theory, but start at the basics of what ABR is and why we. use it. He covers how it delivers a whole series os separate streams at different resolutions and bitrates. Whilst that works well, he quickly starts to show the downsides of ‘static’ ABR profiles. These are where a provider decides that all assets will be encoded at the same set bitrate of 6 or 7 bitrates even though some titles such as cartoons will require less bandwidth than sports programmes. This is where per-title and other encoding techniques come in.
Netflix coined the term ‘per-title encoding’ which has since been called content-aware encoding. This takes in to consideration the content itself when determining the bitrate to encode at. Using automatic processes to determine objective quality of a sample encode, it is able to determine the optimum bitrate.
Content & network-aware encoding takes into account the network delivery as part of the optimisation as well as the quality of the final video itself. It’s able to estimate the likelihood of a stream being selected for playback based upon its bitrate. The trick is combining these two factors simultaneously to find the optimum bitrate vs quality.
The last element to add in order to make this ABR optimisation as realistic as practical is to take into account the way people actually view the content. Looking at a real example from the US open, we see how on PCs, the viewing window can be many different sizes and you can calculate the probability of the different sizes being used. Furthermore we know there is some intelligence in the players where they won’t take in a stream with a resolution which is much bigger than the browser viewport.
Yuriy brings starts the final section of his talk by explaining that he brought in another quality metric from Westerink & Roufs which allows him to estimate how people see video which has been encoded at a certain resolution which is then scaled to a fixed interim resolution for decoding and then to the correct size for the browser windows.
The result of adding in this further check shows that fewer points on the ladder tend to be better, giving an overall higher quality value. Going much beyond 3 is typically not useful for the website. Shows only a few resolutions needed to get good average quality. Adding more isn’t so useful.
Yuriy finishes by introducing SSIM modeling of the noise of an encoder at different bitrates. Bringing together all of these factors, modelled as equations, allows him to suggest optimal ABR ladders.
Why is CMAF still ‘the future’ of OTT? Published in 2018, CMAF’s been around for a while now so what are the challenges and hurdles holding up implementation? Are there reasons not to use it at all? CMAF is a way of encoding and packaging media which then can be sent using MPEG DASH and HLS, the latter being the path Disney+ has chosen, for instance.
This panel from Streaming Media West Connect, moderated by Jan Ozer, discusses CMAF use within Akami, Netflix, Disney+, and Hulu. Peter Chave from Akamai starts off making the point that CMAF is important to CDNs because if companies are able to use just one CMAF file as the source for different delivery formats, this reduces storage costs for consumers and makes each individual file more popular thus increasing the chance of having a file available in the CDN (particularly at the edge) and reducing cache misses. They’ve had to do some work to ensure that CMAF is carried throughout the CDN efficiently and ensuring the manifests are correctly checked.
Disney+, explains Bill Zurat, is 100% HLS CMAF. Benefiting from the long experience of the Disney Streaming Services teams (formerly BAMTECH), but also from setting up a new service, Disney were able to bring in CMAF from the start. There are issues ensuring end-device support, but as part of the launch, a number were sunsetted which didn’t have the requirements necessary to support either the protocol or the DRM needed.
Hulu is an aggregator so they have strong motivation to normalise inputs, we hear from Hulu’s Nick Brookins. But they also originate programming along with live streaming so CMAF has an important to play on the way in and the way out. Hulu dynamically regenerates their manifests so can iterate as they roll out easily. They are currently part the way through the rollout and will achieve full CMAF compatibility within the next 18 months.
The conversation turns to DRM. CMAF supports two methods of DRM known as CTR (adopted by Apple) and CBC (also known as CBCS) which has been adopted by others. AV1 supports both, but the recommendation has been to use CBC which appears have been universally followed to date explains Netflix’s Cyril Concolato. Netflix have been using AV1 since it was finalised and are aiming to have most titles transitioned by 2021 to CMAF.
Peter comments from Akamai’s position that they see a number of customers who, like Disney+ and Peacock, have been able to enter the market recently and move straight into CMAF, but there is a whole continuum of companies who are restricted by their workflows and viewer’s devices in moving to CMAF.
Low latency streaming is one topic which invigorates minds and debates for many in the industry. Netflix, being purely video on demand, they are not interested in low-latency streaming. However, Hulu is as is Disney Streaming Services, but Bill cautions us on rushing to the bottom in terms of latency. Quality of experience is improved with extra latency both in terms of reduced rebuffering and, in some cases, picture quality. Much of Disney Streaming Services’ output needs to match cable, rather than meeting over-the-air latencies or less.
The panel session finishes with a quick-fire round of questions from Jan and the audience covering codec strategy, whether their workflows have changed to incorporate CMAF, just-in-time vs static packaging, and what customers get out of CMAF.
Streaming can seem deceptively simple and a simple HLS workflow can be, but to deliver a monetised service to a wide range of devices, with a mix of live and on-demand assets, with advertising and DRM where needed is far from trivial. In this video, we hear from several companies on how they manage the complexity which allows their service to thrive.
Nadine Krefetz from streaming media asks the questions as we hear from Sinclair, Eyevinn Technology, fuboTV, FandangoNOW and Verizon Media. Firstly they introduce us to their services and the types of workflows that they are maintaining day in, day out.
Companies like Sinclair are frequently adding new channels through market acquisitions. Those companies that don’t grow through acquisition will, similarly, find themselves looking at their own legacy workflows as they look to modernise and improve their offering. Our panel gives their thoughts on tackling this situation. Magnus Svensson and Michael E. Bouchard both talk about having a blueprint, in essence, a generic workflow which contains all the functional blocks needed for a streaming service. You can then map the old and new workflows to the blueprint and plan migration and integration points around that.
The panel covers questions about how smaller services can address Roku and Amazon Fire devices, what to ask when launching a new service and which parts of their services would they never want to buy in or outsource.
Ad insertion is a topic which is essential and complex. Server-Side Ad Insertion (SSAI) is seen as an essential technology for many services as it provides protection against adblockers and can offer more tight management of how and when viewers see ads. But the panel has seen that ad revenues are lower for SSAI since there are fewer analytics data points returned although VAST 4.0 is addressing this problem. This has led to one of the panel members going back to client-side ads for some of their workflows simply due to revenue. Magnus Svensson points out that preparation is key for advertising: Ensuring all adverts are in the correct formats and have the right markers, having slides ready and pre-loading to reduce peaks during live transmissions.
The panel closes looking at their biggest challenges, often in adapting to the pandemic, and the ever-evolving landscape of transport formats. Watch now! Speakers
Michael E. Bouchard
Vice President of Technology Strategy,
ONE Media, Sinclair Broadcast Group
Media Solution Specialist,
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.
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