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

Abstract

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.

Measuring Key Quality Indicators in Cloud Gaming: Framework and Assessment Over Wireless Networks

“…Considering this scenario, the client requirements are reduced in comparison with a traditional online gaming client. This fact, let this element be called a “thin client” [ 7 , 26 ]. Nevertheless, the online gaming case is not directly applicable to the Cloud Gaming approach.…”

“…Due to the need of sending multimedia data which has been rendered in the server, a user’s connection must be able to send a large amount of data that conforms these content with the lowest possible latency, like in online gaming [ 6 ]. Hence, this kind of services are more network sensitive, which requires to assure a set of specific requirements that allow providing the best possible Quality of Experience (QoE) [ 4 , 7 ].…”

Congestion Control for Cloud Gaming Over UDP Based on Round-Trip Video Latency

“…More recently, Hong et al [23] proposed a MOS (Mean Opinion Score) based model for dynamic frame rate and bitrate adaptation on the open-source CG platform GamingAnywhere [24]. The model implicitly considers the available bandwidth, which estimation is inspired in WBest [25]; it keeps track of the dispersion time of the video packets but does not consider a threshold over the latency.…”

QoE Management for Future Networks

“…The QoE requirements of those applications need to be evaluated in order to facilitate QoE optimization. [32] proposes a QoE management approach for Cloud gaming, which can be seen as one representative in that direction. Furthermore, it shows that the generic functions of the NAM model also suit for applications to compare different NAM solutions and to investigate the impact of different monitoring and control capabilities on QoE-IFs, the authors set up a measurement environment that implements the key building blocks and facilitates the interaction between the involved entities.…”