QAVA: Quota aware video adaptation

Jiasi Chen, Amitabha Ghosh, Josphat Magutt, Mung Chiang

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

22 Scopus citations

Abstract

Two emerging trends of Internet applications, video traffic becoming dominant and usage-based pricing becoming prevalent, are at odds with each other. Given this conflict, is there a way for users to stay within their monthly data plans (data quotas) without suffering a noticeable degradation in video quality? In this work, we develop an online video adaptation system, called Quota Aware Video Adaptation (QAVA), that manages this tradeoff by leveraging the compressibility of videos and by predicting consumer usage behavior throughout a billing cycle. We propose the QAVA architecture and develop its main modules, including Stream Selection, User Profiling, and Video Profiling. Online algorithms are designed through dynamic programming and evaluated using real video request traces. Empirical results suggest that QAVA can provide an effective solution to the dilemma of usage-based pricing of heavy video traffic.

Original languageEnglish (US)
Title of host publicationCoNEXT 2012 - Proceedings of the 2012 ACM Conference on Emerging Networking Experiments and Technologies
Pages121-132
Number of pages12
DOIs
StatePublished - Dec 1 2012
Event8th ACM International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2012 - Nice, France
Duration: Dec 10 2012Dec 13 2012

Publication series

NameCoNEXT 2012 - Proceedings of the 2012 ACM Conference on Emerging Networking Experiments and Technologies

Other

Other8th ACM International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2012
CountryFrance
CityNice
Period12/10/1212/13/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Keywords

  • Data quota
  • Video rate adaptation
  • Video streaming

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