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


We have a tendency to study the matter of optimally adapting ongoing cloud gaming sessions to maximise the gamer expertise in dynamic environments. The thought of problem is kind of difficult because: 1) gamer experience is subjective and exhausting to quantify; a pair of) the existing open-source cloud gaming platform does not support dynamic reconfigurations of video codecs; and three) the resource allocation among concurrent gamers leaves an enormous room to optimize. We rigorously address these three challenges by: one) 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 nevertheless efficient algorithms to maximize the general gaming expertise or guarantee the fairness among gamers. We tend to conduct in depth trace-driven simulations to demonstrate the deserves of our algorithms and implementation. Our simulation results show that the proposed efficient algorithms: 1) outperform the baseline algorithms by up to forty six% and thirty%; 2) run quick and scale to large (≤8000 gamers) issues; and three) achieve the user-specified optimization criteria, such as maximizing average gamer experience or maximizing the minimum gamer experience. The ensuing cloud gaming platform will be leveraged by many researchers, developers, and gamers.

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