Buffer-Based Reinforcement Learning for Adaptive Streaming

Yue Zhang, Yao Liu

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

7 Scopus citations


Adaptive streaming improves user-perceived quality by altering the streaming bitrate depending on network conditions, trading reduced video bitrates for reduced stall times. Existing adaptation approaches, e.g., rate-based, buffer-based, either rely heavily on accurate bandwidth prediction or can be overly-conservative about video bitrates. In this work, we propose a reinforcement learning approach to choose the segment quality during playback. This approach uses only the buffer state information and optimizes for a measure of user-perceived streaming quality. Simulation results show that our proposed approach achieves better QoE than rate-, buffer-based approaches, as well as other reinforcement learning approaches.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
EditorsKisung Lee, Ling Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages2
ISBN (Electronic)9781538617915
StatePublished - Jul 13 2017
Externally publishedYes
Event37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - Atlanta, United States
Duration: Jun 5 2017Jun 8 2017

Publication series

NameProceedings - International Conference on Distributed Computing Systems


Other37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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