An Intelligent Prefetch Strategy with Multi-round Cell Enhancement in Volumetric Video Streaming
Suqing Liu, Guanghui Zhang, Mengbai Xiao, Dongxiao Yu, Xiuzhen Cheng · 2024
Over the past few years, volumetric video streaming, a cutting-edge application in virtual reality (VR) and augmented reality (AR), has gained significant attention. However, its enormous bandwidth demands exceed the capabilities of current networks to support full-size transmission. As a result, streaming vendors in the industry typically use field-of-view (FoV) prediction to reduce the streaming video size. While this approach makes transmission feasible, our measurements have shown that the adopted sequential video prefetch mechanism significantly hinders streaming performance. To address this challenge, we propose PACE, an intelligent video prefetch strategy that leverages multi-round cell-enhanced downloading. PACE divides the prefetching process for each group-of-frame (GoF) into multiple rounds, downloading different cells in each round based on periodically updated FoV prediction results. This method allows the FoV prediction to operate independently of the prefetch length limitations. Additionally, PACE incorporates a greedy-policy-based video quality decision algorithm to fully utilize network bandwidth and maximize the quality of experience (QoE). Extensive evaluations demonstrate that PACE can enhance FoV prediction accuracy by up to 23.1% and improve QoE performance by up to 54.8% while showing strong robustness across a wide range of streaming environments.