Adaptive Cloud VR Gaming Optimized by Gamer QoE Models

Abstract

Cloud Virtual Reality (VR) gaming offloads computationally-intensive VR games to resourceful data centers. However, ensuring good Quality of Experience (QoE) in cloud VR gaming is inherently challenging as VR gamers demand high visual quality, short response time, and negligible cybersickness. In this article, we study the QoE of cloud VR gaming and build a QoE-optimized system in a few steps. First, we establish a cloud VR gaming testbed capable of emulating various network conditions. Using the testbed, we conduct comprehensive QoE evaluations using a user study to evaluate the influence of diverse factors, such as encoding settings, network conditions, and game genres, on gamer QoE scores. Second, we construct the very first QoE models for cloud VR gaming using our QoE evaluation results. Our QoE models achieve up to 0.93 ( \(\sigma=0.02\) ) in Pearson Linear Correlation Coefficient (PLCC) and 0.92 ( \(\sigma=0.02\) ) in Spearman Rank-Order Correlation Coefficient (SROCC), where \(\sigma\) stands for the standard deviation. Last, we leverage our QoE models for dynamically adapting encoding settings in our testbed. Extensive experiments revealed that, compared to the current practice, our adaptive cloud VR gaming system improves: (i) overall quality by 0.87 ( \(\sigma=0.44\) ), (ii) visual quality by 0.61 ( \(\sigma=0.45\) ), and (iii) interaction quality by 1.20 ( \(\sigma=0.48\) ) on average in 5-point Mean Opinion Score (MOS).