Optimal Volumetric Video Streaming With Hybrid Saliency Based Tiling

Volumetric video enables a six-degree-of-freedom (6DoF) immersive viewing experience and has a wide range of applications in entertainment and education, among others. Most existing approaches to volumetric video streaming are extensions of VR video streaming solutions that do not take into account user behavior and the properties of the video during the tiling process, and the complexity of decoding is high. To this end, we study volumetric video streaming in this paper and address the research questions mentioned above. In particular, we first propose a hybrid visual saliency and hierarchical clustering empowered 3D tiling scheme that better matches the user's field of view (FoV). Then, we build a quality of experience (QoE) model considering the volumetric video features as the optimization objective. In addition to the usual encoded version, we introduce the reconstructed version (i.e., decoded version, which allows the user to skip the decoding process and thus reduces the decoding overhead) and propose a joint computational and communication resource allocation scheme to achieve a trade-off between communication and computational resources to maximize the QoE. We perform exhaustive simulations and build a prototype system to verify the performance of the proposed tiling and transmission scheme. The results show that the proposed tiling and transmission scheme performs significantly better than the comparison schemes.