VIDEOCHEF: Efficient approximation for streaming video processing pipelines

Ran Xu, Jinkyu Koo, Rakesh Kumar, Peter Bai, Subrata Mitra, Sasa Misailovic, Saurabh Bagchi

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

Abstract

Many video streaming applications require low-latency processing on resource-constrained devices. To meet the latency and resource constraints, developers must often approximate filter computations. A key challenge to successfully tuning approximations is finding the optimal configuration, which may change across and within the input videos because it is content-dependent. Searching through the entire search space for every frame in the video stream is infeasible, while tuning the pipeline offline, on a set of training videos, yields suboptimal results. We present VIDEOCHEF, a system for approximate optimization of video pipelines. VIDEOCHEF finds the optimal configurations of approximate filters at runtime, by leveraging the previously proposed concept of canary inputs-using small inputs to tune the accuracy of the computations and transferring the approximate configurations to full inputs. VIDEOCHEF is the first system to show that canary inputs can be used for complex streaming applications. The two key innovations of VIDEOCHEF are (1) an accurate error mapping from the approximate processing with downsampled inputs to that with full inputs and (2) a directed search that balances the cost of each search step with the estimated reduction in the run time. We evaluate our approach on 106 videos obtained from YouTube, on a set of 9 video processing pipelines with a total of 10 distinct filters. Our results show significant performance improvement over the baseline and the previous approach that uses canary inputs. We also perform a user study that shows that the videos produced by VIDEOCHEF are often acceptable to human subjects.

Original languageEnglish (US)
Title of host publicationProceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018
PublisherUSENIX Association
Pages43-55
Number of pages13
ISBN (Electronic)9781939133021
StatePublished - Jan 1 2020
Event2018 USENIX Annual Technical Conference, USENIX ATC 2018 - Boston, United States
Duration: Jul 11 2018Jul 13 2018

Publication series

NameProceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018

Conference

Conference2018 USENIX Annual Technical Conference, USENIX ATC 2018
CountryUnited States
CityBoston
Period7/11/187/13/18

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Xu, R., Koo, J., Kumar, R., Bai, P., Mitra, S., Misailovic, S., & Bagchi, S. (2020). VIDEOCHEF: Efficient approximation for streaming video processing pipelines. In Proceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018 (pp. 43-55). (Proceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018). USENIX Association.