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.

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.