P 2 VS: Progressive Partition-Based Volumetric Video Streaming under Network Dynamics

作者：Jingrou Wu, Haoxian Liu, Jin Zhang, Dan Wang, Jing Jiang · 年份：2025 · DOI：10.1145/3746027.3755403 · 被引用次数：1 · 研究领域：Video Coding and Compression Technologies、Image and Video Quality Assessment、Advanced Steganography and Watermarking Techniques

Volumetric videos are essential for immersive applications due to their engaging and realistic experiences. However, streaming them in real time over constrained, fluctuating networks remains challenging. Progressive streaming is an effective method to mitigate this issue by gradually enhancing video quality through incremental data transmission. However, existing progressive volumetric streaming solutions often rely on specific compression algorithms or require codec modifications, leading to poor compatibility with standard codecs. In this paper, we propose P2VS, a progressive partition-based volumetric video streaming framework, to achieve codec-independent progressive streaming. Specifically, P2VS leverages the unique structure of point cloud-based volumetric video to incrementally enhance video quality without being constrained by specific compression algorithms. Moreover, we propose adaptive streaming algorithms under this framework to enhance the quality of experience (QoE). Extensive simulations demonstrate that P2VS improves QoE by 21% on average compared to non-progressive streaming schemes. It also achieves better bandwidth efficiency and full compatibility with standard codecs. A prototype is built to verify the feasibility of P2VS.