Quality-aware strategies for optimizing ABR video streaming QoE and reducing data usage

Yanyuan Qin, Shuai Hao, Krishna R. Pattipati, Feng Qian, Subhabrata Sen, Bing Wang, Chaoqun Yue

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

26 Scopus citations


Streaming videos over cellular networks is highly challenging. Since cellular data is a relatively scarce resource, many video and network providers offer options for users to exercise control over the amount of data consumed by video streaming. Our study shows that existing data saving practices for Adaptive Bitrate (ABR) videos are suboptimal: they often lead to highly variable video quality and do not make the most effective use of the network bandwidth. We identify underlying causes for this and propose two novel approaches to achieve better tradeoffs between video quality and data usage. The first approach is Chunk-Based Filtering (CBF), which can be retrofitted to any existing ABR scheme. The second approach is QUality-Aware Data-efficient streaming (QUAD), a holistic rate adaptation algorithm that is designed ground up. We implement and integrate our solutions into two video player platforms (dash.js and ExoPlayer), and conduct thorough evaluations over emulated/commercial cellular networks using real videos. Our evaluations demonstrate that compared to the state of the art, the two proposed schemes achieve consistent video quality that is much closer to the user-specified target, lead to far more efficient data usage, and incur lower stalls.

Original languageEnglish (US)
Title of host publicationProceedings of the 10th ACM Multimedia Systems Conference, MMSys 2019
PublisherAssociation for Computing Machinery, Inc
Number of pages12
ISBN (Electronic)9781450362979
StatePublished - Jun 18 2019
Event10th ACM Multimedia Systems Conference, MMSys 2019 - Amherst, United States
Duration: Jun 18 2019Jun 21 2019

Publication series

NameProceedings of the 10th ACM Multimedia Systems Conference, MMSys 2019


Conference10th ACM Multimedia Systems Conference, MMSys 2019
Country/TerritoryUnited States

Bibliographical note

Funding Information:
We thank the anonymous reviewers who gave valuable feedback to improve this work, and our shepherd, Roger Zimmermann, for guiding us through the revisions. The work of Feng Qian was partially supported by NSF under award CNS-1750890.

Publisher Copyright:
© 2019 ACM.


  • Adaptive video streaming
  • Data saving
  • QoE
  • Quality-aware


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