A Smarter Approach to Streaming

Jérôme Vieron, PhD - Director of Research & Innovation for ATEME shows how the broadcast industry can deliver higher quality of OTT streaming services, by using AI for content adaptive streaming.
Jérôme Vieron, PhD - Director of Research & Innovation for ATEME
Jérôme Vieron, PhD - Director of Research & Innovation for ATEME


As viewers, we’ve grown to value choice and flexibility – and thanks to the explosion of competing on-demand and over-the-top (OTT) services we now take anytime, anywhere, any device viewing for granted. Last year, 12.6 billion hours of content were viewed using OTT services, more than double that of the year before.

Whereas before we learnt to love the only content on offer, now we can pick and choose. As a result, the competition to provide the most compelling movies and box-sets is fierce. However, there is a further differentiator. We also have high expectations of the quality of service provided too
These expectations have been raised even further thanks to various new formats including 4K/UHD, HDR and HFR.

One thing is certain; this demand is powering a continuous string of innovations, especially around the issue of streaming, and the most recent of these embraces artificial intelligence (AI). Once the subject of Hollywood movies, AI is now being used by global tech giants such as Amazon and Google to predict the behaviours of its users, with health organisations like the NHS also looking into the technology to help alleviate pressure from its doctors and nurses.

The broadcast industry is also seeing a more widespread adoption of AI, with it being used in real life to analyse thousands of assets as part of the streaming process. In doing this, AI has been shown to save operators around 30% of content delivery costs, while also improving the quality of this delivery.

Most operators now find that traditional streaming can result in buffering and other delays. Research by Conviva shows that while watching a half-hour show, the average viewer spends less than 18 seconds waiting for a video to rebuffer, however, even this short time is too long when consumer expectations are high and the market so competitive.

The current conversation within the industry looks to be around adaptive streaming and its successor, content adaptive streaming.  Adaptive streaming works by detecting a user's bandwidth and CPU capacity in real time and adjusting the quality of a video stream accordingly. Although the former is widely used, it does mean that for half the content the bitrate will be too high, and for the other half it will be too low. If it’s too high the content may stall and means that the content is never fully optimised.

As a result, industry pioneers such as Netflix have been working on remedying this shortfall. Netflix has been leading the way with per-title encoding and even recently announced per-shot encoding, but these are proprietary technologies and not available to other operators.

Recognising this shortfall, other developers have been working on content that adjusts the bitrates based on the complexity of content rather than just the internet connection. The result is content adaptive streaming which uses AI to compute all the necessary information, such as motion estimation, to make intelligent allocation decisions. Using a variable bitrate to reach constant quality allows bits to be saved when the complexity drops on slow scenes – using fewer profiles on easier content.

The traditional approach is to keep chunks at fixed lengths. The ecosystem usually requires chunks to start with an I-frame so that profile switches can occur between chunks, but with fixed-size chunks this implies arbitrary I-frame placement. Therefore, a scene cut before a chunking point results in a major compression inefficiency as the image is encoded twice.

Content adaptive streaming combines a scene cut detection algorithm in the video encoder with rules to keep chunk size reasonable and minimise drift, in order to prepare the asset for more efficient packaging. This not only brings cost saving benefits due to reduced traffic, storage and other overheads, but also improves the quality of experience for the consumer.

Content adaptive streaming solutions have been developed with interoperability in mind, so individual parameters such as dynamic chunking can be turned on and off. Operators also have the option to use the specific resolutions they want, even if these appear to be suboptimal to the system.

In reality, the use of AI in the industry isn’t new but any technology that enhances the viewing experience for subscribers will be welcomed. Content Adaptive Streaming does this, while at the same time significantly cutting overheads, so pleasing both the viewer and the operator at the same time.

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