PACE: A Multi-Round Cell-Enhanced Prefetching Strategy in Volumetric Video Streaming

In recent years, volumetric video streaming has emerged as a key application in the field of virtual reality (VR) and augmented reality (AR), attracting growing research and industry interest. Despite its potential, the extremely high bandwidth requirements of volumetric content far exceed the capacity of current networks to support full-resolution streaming. To address this limitation, industry practitioners often employ field-of-view (FoV) prediction to downscale the streaming content based on user gaze direction. Although this approach makes the streaming feasible, our empirical measurement reveals that, working with the FoV prediction, the commonly used sequential prefetching mechanism severely constrains streaming efficiency. To overcome this bottleneck, we introduce PACE, a smart prefetching strategy built on multi-round cell-level download scheduling. PACE divides the prefetching process of each group-of-frames (GoF) into several rounds, where distinct video cells are downloaded in each round according to periodically updated FoV predictions. This design decouples FoV prediction from the constraint of prefetch length, enabling more adaptive and responsive streaming. Moreover, PACE integrates a greedy quality selection policy that dynamically adjusts video quality to make full use of available bandwidth and enhance the overall quality of experience (QoE). Comprehensive evaluations show that PACE improves FoV prediction accuracy by up to 23.1% and boosts QoE by as much as 54.8%, while maintaining robust performance across diverse network and user scenarios.