Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming

Zhuqi Li, Yaxiong Xie, Ravi Netravali, Kyle Jamieson

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

5 Scopus citations

Abstract

Short video streaming applications have recently gained substantial traction, but the non-linear video presentation they afford swiping users fundamentally changes the problem of maximizing user quality of experience in the face of the vagaries of network throughput and user swipe timing. This paper describes the design and implementation of Dashlet, a system tailored for high quality of experience in short video streaming applications. With the insights we glean from an in-the-wild TikTok performance study and a user study focused on swipe patterns, Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users' swipe statistics to determine the pre-buffering order and bitrate. The net result is a system that outperforms TikTok by 28-101%, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
PublisherUSENIX Association
Pages1583-1598
Number of pages16
ISBN (Electronic)9781939133335
StatePublished - 2023
Event20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023 - Boston, United States
Duration: Apr 17 2023Apr 19 2023

Publication series

NameProceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023

Conference

Conference20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
Country/TerritoryUnited States
CityBoston
Period4/17/234/19/23

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

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