具有完美、不完美和未知FoV观看概率的360 VR视频的最佳流传输
Optimal Streaming of 360 VR Videos with Perfect, Imperfect and Unknown FoV Viewing Probabilities
摘要：
在本文中，我们研究无线流的多质量平铺360虚拟现实（VR）视频从多天线服务器到多个单天线用户在多载波系统。为了捕获视场（FoV）预测的影响，我们考虑FoV观看概率分布的三种情况，即，完美、不完美和未知FoV观看概率分布，并使用平均总效用、最差平均总效用和最差总效用作为相应的性能度量。我们采用速率分割与连续解码，以有效地将不同360 VR视频的多组瓦片传输给其请求用户。在每种情况下，我们优化瓦片的编码率、FoV的最小编码率、公共和私有消息的速率以及传输波束成形向量，以最大化总效用。这三种情况下的问题都是具有挑战性的非凸优化问题。我们成功地将每种情况下的问题转化为一个可微的目标函数的凸（DC）规划问题的差异，并获得一个次优解使用凹凸程序（CCCP）。最后，数值结果表明，所提出的解决方案实现了显着的收益在所有三种情况下，现有的计划。据我们所知，这是第一项揭示FoV预测及其准确性对多质量平铺360 VR视频流传输性能的影响的工作。
英文摘要：
In this paper, we investigate wireless streaming of multi-quality tiled 360 virtual reality (VR) videos from a multi-antenna server to multiple single-antenna users in a multi-carrier system. To capture the impact of field-of-view (FoV) prediction, we consider three cases of FoV viewing probability distributions, i.e., perfect, imperfect and unknown FoV viewing probability distributions, and use the average total utility, worst average total utility and worst total utility as the respective performance metrics. We adopt rate splitting with successive decoding for efficient transmission of multiple sets of tiles of different 360 VR videos to their requesting users. In each case, we optimize the encoding rates of the tiles, minimum encoding rates of the FoVs, rates of the common and private messages and transmission beamforming vectors to maximize the total utility. The problems in the three cases are all challenging nonconvex optimization problems. We successfully transform the problem in each case into a difference of convex (DC) programming problem with a differentiable objective function, and obtain a suboptimal solution using concave-convex procedure (CCCP). Finally, numerical results demonstrate the proposed solutions achieve notable gains over existing schemes in all three cases. To the best of our knowledge, this is the first work revealing the impact of FoV prediction and its accuracy on the performance of streaming of multi-quality tiled 360 VR videos.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
