This paper investigates adaptive streaming of one or multiple tiled 360 videos from a multi-antenna base station (BS) to one or multiple single-antenna users, respectively, in a multi-carrier wireless system. We aim to maximize the video quality while keeping rebuffering time small via encoding rate adaptation at each group of pictures (GOP) and transmission adaptation at each (transmission) slot. 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. In the single-user scenario, we optimize the encoding rates of the tiles, encoding rates of the FoVs, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. In the multi-user scenario, we adopt rate splitting with successive decoding and optimize the encoding rates of the tiles, encoding rates of the FoVs, rates of the common and private messages, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. Then, we separate the challenging optimization problem into multiple tractable problems in each scenario. In the single-user scenario, we obtain a globally optimal solution of each problem using transformation techniques and the Karush-Kuhn-Tucker (KKT) conditions. In the multi-user scenario, we obtain a KKT point of each problem using the concave-convex procedure (CCCP). Finally, numerical results demonstrate that 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 on the performance of adaptive streaming of tiled 360 videos.

中文翻译：

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无线网络中具有完美、不完美和未知视场观看概率的 360 度视频自适应流媒体

本文研究了在多载波无线系统中从多天线基站 (BS) 向一个或多个单天线用户分别传输一个或多个平铺 360 度视频的自适应流。我们的目标是通过每组图片 (GOP) 的编码率适配和每个（传输）时隙的传输适配来最大化视频质量，同时保持较小的重新缓冲时间。为了捕捉视野 (FoV) 预测的影响，我们考虑三种 FoV 观看概率分布情况，即完美、不完美和未知的 FoV 观看概率分布，并使用平均总效用、最差平均总效用、和最差的总效用作为各自的性能指标。在单用户场景中，我们优化了图块的编码率、FoV 的编码率、以及所有子载波的传输波束成形向量，以最大化每种情况下的总效用。在多用户场景中，我们采用连续解码的速率拆分，并优化所有子载波的瓦片编码率、FoV 编码率、公共和私人消息率以及传输波束成形向量，以最大化总效用每个案例。然后，我们将具有挑战性的优化问题分解为每个场景中的多个易处理问题。在单用户场景中，我们使用转换技术和 Karush-Kuhn-Tucker (KKT) 条件获得每个问题的全局最优解。在多用户场景中，我们使用凹凸过程（CCCP）获得每个问题的 KKT 点。最后，数值结果表明，在所有三种情况下，所提出的解决方案都比现有方案取得了显着的收益。据我们所知，这是第一项揭示 FoV 预测对 360 度全景视频自适应流媒体性能影响的工作。