Many XR applications require the delivery of volumetric video to users with six degrees of freedom (6-DoF) movements. Point Cloud has become a popular volumetric video format. A dense point cloud consumes much higher bandwidth than a 2D/360 degree video frame. User Field of View (FoV) is more dynamic with 6-DoF movement than 3-DoF movement. To save bandwidth, FoV-adaptive streaming predicts a user's FoV and only downloads point cloud data falling in the predicted FoV. However, it is vulnerable to FoV prediction errors, which can be significant when a long buffer is utilized for smoothed streaming. In this work, we propose a multi-round progressive refinement framework for point cloud video streaming. Instead of sequentially downloading point cloud frames, our solution simultaneously downloads/patches multiple frames falling into a sliding time-window, leveraging the inherent scalability of octree-based point-cloud coding. The optimal rate allocation among all tiles of active frames are solved analytically using the heterogeneous tile rate-quality functions calibrated by the predicted user FoV. Multi-frame downloading/patching simultaneously takes advantage of the streaming smoothness resulting from long buffer and the FoV prediction accuracy at short buffer length. We evaluate our streaming solution using simulations driven by real point cloud videos, real bandwidth traces, and 6-DoF FoV traces of real users. Our solution is robust against the bandwidth/FoV prediction errors, and can deliver high and smooth view quality in the face of bandwidth variations and dynamic user and point cloud movements.

翻译：许多扩展现实（XR）应用需要向用户提供支持六自由度（6-DoF）运动的体积视频传输。点云已成为一种流行的体积视频格式。密集点云消耗的带宽远高于二维/360度视频帧。与三自由度（3-DoF）运动相比，六自由度运动下用户视场（FoV）具有更高的动态性。为节省带宽，视场自适应流传输技术通过预测用户视场，仅下载落入预测视场内的点云数据。然而，该方法易受视场预测误差影响——当采用长缓冲区实现平滑流传输时，误差可能显著增大。本研究提出一种面向点云视频流传输的多轮渐进式优化框架。该方案不采用顺序逐帧下载的方式，而是利用八叉树点云编码固有的可扩展性，同步下载/修补滑动时间窗口内的多个帧。通过用户视场预测校准的异构分块率-质量函数，我们解析求解了所有活动帧中分块的最优码率分配方案。多帧同步下载/修补策略可兼顾长缓冲区带来的流传输平滑性优势与短缓冲区下视场预测的高精度特性。我们利用真实点云视频数据、实际带宽轨迹及真实用户的六自由度视场轨迹驱动仿真，对所提流传输方案进行评估。实验表明，该方案对带宽/视场预测误差具有鲁棒性，能在带宽波动及用户与点云动态运动情境下提供高质量且平滑的视觉体验。