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


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: (i) gamer experience is subjective and hard to quantify, (ii) the existing open-source cloud gaming platform does not support dynamic reconfigurations of video codecs, and (iii) the resource allocation among concurrent gamers leaves a huge room to optimize. We rigorously address these three challenges by: (i) conducting a crowdsourced user study over the live Internet for an empirical gaming experience model, (ii) enhancing the cloud gaming platform to support frame rate and bitrate adaptation on-the-fly, and (iii) 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: (i) outperform the baseline algorithms by up to 46% and 30%, (ii) run fast and scale to large (≥ 8000 gamers) problems, and (iii) 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.


Hua-Jun Hong, Chih-Fan Hsu, Tsung-Han Tsai, Chun-Ying Huang, Kuan-Ta Chen, and Cheng-Hsin Hsu, "Enabling Adaptive Cloud Gaming in an Open-Source Cloud Gaming Platform," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 25, Issue 12, pp. 2078--2091, Dec. 2015.


@ARTICLE{hsu15:adaptive_cgaming, AUTHOR = {Cheng-Hsin Hsu and Hua-Jun Hong and Chih-Fan Hsu and Tsung-Han Tsai and Chun-Ying Huang and Kuan-Ta Chen}, TITLE = {Enabling Adaptive Cloud Gaming in an Open-Source Cloud Gaming Platform}, JOURNAL = {IEEE Transactions on Circuits and Systems for Video Technology}, YEAR = {2015}, PAGES = {2078-2091}, VOL = {25}, NO = {12}, MONTH = {Dec} }