QoE-Based Server Selection for Mobile Video Streaming

Daniel Kanba Tapang, Siqi Huang, Xueqing Huang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)


Mobile devices make up the bulk of clients that stream video content over the internet. Improving one of the most popular services, i.e., mobile video streaming, has the potential to make the most market impact. Video streaming giants like YouTube, Netflix, Hulu, and Amazon video aim to provide the best quality service and expand market share. The problem of selecting the best server is critical for ensuring the qualified experience for video streaming on a mobile device. Traditional server selection strategies use proximity as a server selection rule. Improved strategies select servers by considering more factors that also impact the quality of experience (QoE). Currently, reinforcement learning is being used to maximize QoE when selecting servers. This paper seeks to further develop an RL agent that performs better on mobile devices. The result is an RL agent that quickly learns to select servers that offer the best QoE.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728159430
Publication statusPublished - Nov 2020
Externally publishedYes
Event5th IEEE/ACM Symposium on Edge Computing, SEC 2020 - Virtual, San Jose, United States
Duration: 11 Nov 202013 Nov 2020

Publication series

NameProceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020


Conference5th IEEE/ACM Symposium on Edge Computing, SEC 2020
Country/TerritoryUnited States
CityVirtual, San Jose


  • Q-Learning
  • QoE
  • Reinforcement Learning
  • Server Selection
  • Video Streaming


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