AI News, Using Machine Learning to Improve Streaming Quality at Netflix

Using Machine Learning to Improve Streaming Quality at Netflix

by Chaitanya Ekanadham One of the common questions we get asked is: “Why do we need machine learning to improve streaming quality?” This is a really important question, especially given the recent hype around machine learning and AI which can lead to instances where we have a “solution in search of a problem.” In this blog post, we describe some of the technical challenges we face for video streaming at Netflix and how statistical models and machine learning techniques can help overcome these challenges.

As we expand rapidly to audiences with diverse viewing behavior, operating on networks and devices with widely varying capabilities, a “one size fits all” solution for streaming video becomes increasingly suboptimal.

At Netflix, we observe network and device conditions as well as aspects of the user experience (e.g., video quality) we were able to deliver for every session, allowing us to leverage statistical modeling and machine learning in this space.

A richer characterization of network quality would prove useful for analyzing networks (for targeting/analyzing product improvements), determining initial video quality and/or adapting video quality throughout playback (more on that below).

The quality of experience can be measured in several ways, including the initial amount of time spent waiting for video to play, the overall video quality experienced by the user, the number of times playback paused to load more video into the buffer (“rebuffer”), and the amount of perceptible fluctuation in quality during playback.

This “credit assignment” problem is a well-known challenge when learning optimal control algorithms, and machine learning techniques (e.g., recent advances in reinforcement learning) have great potential to tackle these issues.

Another area in which statistical models can improve the streaming experience is by predicting what a user will play in order to cache (part of) it on the device before the user hits play, enabling the video to start faster and/or at a higher quality.

By combining various aspects of their viewing history together with recent user interactions and other contextual variables, one can formulate this as a supervised learning problem where we want to maximize the model’s likelihood of caching what the user actually ended up playing, while respecting constraints around resource usage coming from the cache size and available bandwidth.

By employing predictive modeling to prioritize device reliability issues, we’ve already seen large reductions in overall alert volume while maintaining an acceptably low false negative rate, which we expect to drive substantial efficiency gains for Netflix’s device reliability team.

The aforementioned problems are a sampling of the technical challenges where we believe statistical modeling and machine learning methods can improve the state of the art: Solving these problems is central to Netflix’s strategy as we stream video under increasingly diverse network and device conditions.

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