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Cloud gaming QoE models for deriving video encoding adaptation strategies

Cloud gaming has been recognized as a promising shift in the online game industry, with the aim being to deliver high-quality graphics games to any type of end user device. The concepts of cloud computing are leveraged to render the game scene as a video stream which is then delivered to players in real-time. Given high bandwidth and strict latency requirements, a key challenge faced by cloud game providers lies in configuring the video encoding parameters so as to maximize player Quality of Experience (QoE) while meeting bandwidth availability constraints. In this paper we address this challenge by conducting a subjective laboratory study involving 52 players and two different games aimed at identifying QoE-driven video encoding adaptation strategies. Empirical results are used to derive analytical QoE estimation models as functions of bitrate and framerate, while also taking into account game type and player skill. Results have shown that under certain identified bandwidth conditions, reductions of framerate lead to QoE improvements due to improved graphics quality. Given that results indicate that different QoE-driven video adaptation policies should likely be applied for different types of games, we further report on objective video metrics that may be used to classify games for the purpose of choosing an appropriate and QoE-driven video codec configuration strategy.