QoE-Aware Resource Allocation for Multiple Cloud Gaming Users Sharing a Bottleneck Link

Abstract

Cloud gaming offers the capability of delivering high-quality graphics games to any type of end user device, however at the cost of high bandwidth consumption and strict latency requirements. Meeting Quality of Experience (QoE) requirements under limited resource availability calls for efficient and dynamic service adaptation. In this paper, we formulate an optimization problem for QoE-aware resource allocation for multiple cloud gaming users sharing a bottleneck link. The optimization problem is solved by utilizing algorithms that employ QoE estimation models derived from subjective studies for different types of games. We specifically investigate the impact on the resource allocation outcome when jointly considering both quality and QoE fairness as optimization objectives. The QoE-aware algorithms are shown to achieve higher average and higher minimum MOS values compared to a baseline algorithm. Results also confirm that both a cloud gaming service provider and resource provider should consider game type when adapting video coding parameters and allocating resources.