  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.
