Video: All you need to know about video KPIs

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

Video: Subjective and Objective Quality Assessment

Video quality is a key part of user experience, so understanding how different parts of your distribution chain can affect your video in different ways is an important factor ensuring continued quality in the service and quick fault finding where problems are reported.

Abdul Rehman from SSIMWAVE speaks at the Kitchener-Warterloo Video Technology Meetup explaining both subjective quality assessment where humans judge the quality of the video and objective quality assessments where computers analyse, often terabytes, of video to assess the quality.

Starting with a video showing examples of different problems that can occur in the chain, Abdul explains how many things can go wrong including lost or delayed data, incorrect content and service configuration checks. Display devices, nowadays, come in many shapes, sizes and resolutions which can, in turn, cause impairments with display as can the player and viewing conditions. These are only around half of the different possibilities which include the type of person – a golden eye, or a pure consumer.

In order to test your system, you may need test codecs and you will need test content. Abdul talks about subject rated databases which have images which have certain types of distortions/impairments. After seeing many examples of problem images, Abdul asks the question of who to deal with natural images which look similar or deliberate use, for creative purposes, of distorted videos.

Subjective video quality assessment is one solution to this since it uses people who are much better at detecting creative quality than computers. As such, this avoids many false positives where video may be judged as bad, but there is intent in the use. Moreover, it also represents direct feedback from your target group. Abdul talks through the different aspects of what you need to control for when using subjective video quality assessment in order to maximise its usefulness and allow results from different sessions and experiments to be directly compared.

This is to be compared against objective video quality assessment where a computer is harnessed to plough through the videos. This can be very effective for many applications meaning it can shine in terms of throughput and number of measurements. Additionally, it can make regression testing very easy. The negatives can be cost, false positives and sometimes speed – depending on the application. You then can take your pick of algorithms such as MS-SSIM, VMAF and others. Abdul finishes by explaining more about the benefits and what to look out for.

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Abdul Rehman Abdul Rehman
Cofounder, CEO and CTO,

Webinar: Assessing Video Quality: Methods, Measurements, and Best Practices

Wednesday, November 13th, 8am PST / 16:00 GMT

Bitmovin have brought together Jan Ozer from the Streaming Learning Center, their very own Sean McCarthy and Carlos Bacquet from SSIM Wave to discuss how best to assess video quality.

Fundamental to assessing video quality, of course, is what we mean by quality, which artefacts are most problematic and what drives the importance of video quality.

Quality of streaming, of course, is interdependent on the quality of the experience in general. Thinking of an online streaming system as a whole, speed of playback, smooth playback on the player itself and rebuffing are all factors of perceived quality as much as the actual codec encoding quality itself which is what is more traditionally measured.

The webinar brings together experience in measuring quality, monitoring systems and ways in which you can derive your own testing to lock on to the factors which matter to you and your business.

See the related posts below for more from Jan Ozer

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Jan Ozer Jan Ozer
Industry Analyst
Jan Ozer
Sean McCarthy Sean McCarthy
Technical Product Marketing Manager,
Carlos Bacquet Carlos Bacquet
Solutions Architect

Video: A Standard for Video QoE Metrics

A standard in progress for quality of experience networks, rebufereing time etc. Under the CTA standards body wanting to create a standard around these metrics. The goal of the group is to come up with a standard set of player events, metrics & terminology around QoE streaming. Concurrent viewers, isn’t that easy to define? If the user is paused, are they concurrently viewing the video? Buffer underruns is called rebuffering, stalling, waiting. Intentionally focussing on what the viewers actually see and experience. QoS is a measurement of how well the platform is performing, not necessarily the same as what they are experiencing.

The standard has ideas of different levels. There are player properties and events which are standardised ways of signalling that certain things are happening. Also Session Metrics are defined which then can feed into Aggregate Metrics. The first set of metrics include things such as playback failure percentage, average playback stalled rate, average startup time and playback rate with the aim of setting up a baseline and to start to get feedback from companies as they implement these, seemingly simple, metrics.

This first release can be found on github.

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Steve Heffernan Steve Heffernan
Co-Founder, Head of Product,