Multi-view videos (MVVs) provide immersive viewing experience, at the cost of traffic load increase for wireless networks. In this paper, we would like to optimize MVV transmission in a multiuser wireless network by exploiting both natural multicast opportunities and view synthesis-enabled multicast opportunities. Specifically, we first establish a mathematical model to specify view synthesis at the server and each user, and characterize its impact on multicast opportunities. This model is highly nontrivial and fundamentally enables the optimization of view synthesis-based multicast opportunities. For given video quality requirements of all users, we consider the optimization of view selection, transmission time and power allocation to minimize the average weighted sum energy consumption for view transmission and synthesis. In addition, under the energy consumption constraints at the server and each user respectively, we consider the optimization of view selection, transmission time and power allocation and video quality selection to maximize the total utility. These two optimization problems are challenging mixed discrete-continuous optimization problems. For the first problem, we propose an algorithm to obtain an optimal solution with reduced computational complexity by exploiting optimality properties. For each problem, to reduce computational complexity, we also propose a low-complexity algorithm to obtain a suboptimal solution, using Difference of Convex (DC) programming. Finally, numerical results show the advantage of the proposed solutions over existing ones, and demonstrate the importance of the optimization of view synthesis-enabled multicast opportunities in MVV transmission.

中文翻译：

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通过利用自然和视图合成启用的多播机会在多用户无线网络中优化多视图视频传输

多视图视频 (MVV) 提供身临其境的观看体验，但代价是无线网络的流量负载增加。在本文中，我们希望通过利用自然多播机会和启用视图合成的多播机会来优化多用户无线网络中的 MVV 传输。具体来说，我们首先建立一个数学模型来指定服务器和每个用户的视图合成，并表征其对多播机会的影响。该模型非常重要，并且从根本上实现了基于视图合成的多播机会的优化。对于所有用户的给定视频质量要求，我们考虑优化视图选择、传输时间和功率分配，以最小化视图传输和合成的平均加权和能量消耗。此外，在服务器和每个用户分别的能量消耗约束下，我们考虑优化视图选择、传输时间和功率分配以及视频质量选择以最大化总效用。这两个优化问题是具有挑战性的混合离散-连续优化问题。对于第一个问题，我们提出了一种算法，通过利用最优性来获得具有降低计算复杂度的最优解。对于每个问题，为了降低计算复杂度，我们还提出了一种低复杂度算法，使用凸差（DC）编程来获得次优解决方案。最后，数值结果显示了所提出的解决方案相对于现有解决方案的优势，