Congestion Control for VR Cloud Gaming : Integration and Comparison in Real VR Gaming Environment

Abstract

Virtual reality (VR) cloud gaming is increasingly developing in the gaming industry. Yet, the performance of the congestion control algorithms on top of which these systems build remains under-explored. In this study, we implement two industry-standard network congestion control algorithms, Google Congestion Control (GCC) and Network-Assisted Dynamic Adaptation (NADA), according to their Requests for Comments (RFCs), and integrate them into an open-source VR gaming system (ALVR). Including ALVR's congestion control (ALVR-ABR), we conduct extensive experiments on real-world networks to evaluate each algorithm's frame latency, target-to-receiving bitrate gap, dropped frames, image quality, and fairness among heterogeneous competing flows. GCC decreases frame latency by 35% compared to NADA and by 42% compared to ALVR. NADA and ALVR-ABR present significant gaps between the selected and received bitrate, causing substantial congestion-induced frame drops, while GCC has a minimal gap, resulting in minor frame drops, suggesting its suitability for game-player interaction. GCC exhibits a 2.7% and 5% decrease in image quality compared to NADA and ALVR-ABR, respectively, indicating slight immersion degradation. However, only NADA ensures a fair bandwidth share against loss-based flows due to its bitrate response to loss-induced congestion signals and lower sensitivity to delay gradients compared to GCC.