Popularity prediction of Facebook videos for higher quality streaming

Linpeng Tang, Qi Huang, Amit Puntambekar, Ymir Vigfusson, Wyatt Lloyd, Kai Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

Streaming video algorithms dynamically select between different versions of a video to deliver the highest quality version that can be viewed without buffering over the client's connection. To improve the quality for viewers, the backing video service can generate more and/or better versions, but at a significant computational overhead. Processing all videos uploaded to Facebook in the most intensive way would require a prohibitively large cluster. Facebook's video popularity distribution is highly skewed, however, with analysis on sampled videos showing 1% of them accounting for 83% of the total watch time by users. Thus, if we can predict the future popularity of videos, we can focus the intensive processing on those videos that improve the quality of the most watch time. To address this challenge, we designed Chess, the first popularity prediction algorithm that is both scalable and accurate. Chess is scalable because, unlike the state-of-the-art approaches, it requires only constant space per video, enabling it to handle Facebook's video workload. Chess is accurate because it delivers superior predictions using a combination of historical access patterns with social signals in a unified online learning framework. We have built a video prediction service, ChessVPS, using our new algorithm that can handle Facebook's workload with only four machines. We find that re-encoding popular videos predicted by ChessVPS enables a higher percentage of total user watch time to benefit from intensive encoding, with less overhead than a recent production heuristic, e.g., 80% of watch time with one-third as much overhead.

Original languageEnglish (US)
Title of host publicationProceedings of the 2017 USENIX Annual Technical Conference, USENIX ATC 2017
PublisherUSENIX Association
Pages111-123
Number of pages13
ISBN (Electronic)9781931971386
StatePublished - Jan 1 2019
Event2017 USENIX Annual Technical Conference, USENIX ATC 2017 - Santa Clara, United States
Duration: Jul 12 2017Jul 14 2017

Publication series

NameProceedings of the 2017 USENIX Annual Technical Conference, USENIX ATC 2017

Conference

Conference2017 USENIX Annual Technical Conference, USENIX ATC 2017
CountryUnited States
CitySanta Clara
Period7/12/177/14/17

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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