Enabling Adaptive Cloud Gaming in an Open-Source Cloud Gaming Platform

Hua Jun Hong, Chih Fan Hsu, Tsung Han Tsai, Chun-Ying Huang, Kuan Ta Chen, Cheng Hsin Hsu

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


We study the problem of optimally adapting ongoing cloud gaming sessions to maximize the gamer experience in dynamic environments. The considered problem is quite challenging because: 1) gamer experience is subjective and hard to quantify; 2) the existing open-source cloud gaming platform does not support dynamic reconfigurations of video codecs; and 3) the resource allocation among concurrent gamers leaves a huge room to optimize. We rigorously address these three challenges by: 1) conducting a crowdsourced user study over the live Internet for an empirical gaming experience model; 2) enhancing the cloud gaming platform to support frame rate and bitrate adaptation on-the-fly; and 3) proposing optimal yet efficient algorithms to maximize the overall gaming experience or ensure the fairness among gamers. We conduct extensive trace-driven simulations to demonstrate the merits of our algorithms and implementation. Our simulation results show that the proposed efficient algorithms: 1) outperform the baseline algorithms by up to 46% and 30%; 2) run fast and scale to large (≤8000 gamers) problems; and 3) achieve the user-specified optimization criteria, such as maximizing average gamer experience or maximizing the minimum gamer experience. The resulting cloud gaming platform can be leveraged by many researchers, developers, and gamers.

Original languageEnglish
Article number7137667
Pages (from-to)2078-2091
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number12
StatePublished - 1 Dec 2015


  • Cloud gaming
  • crowdsourcing
  • optimization
  • real-time streaming
  • resource allocation
  • user study


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