Abstract
Due to the growing need for bandwidth starving Multi-View Videos (MVV) in virtual reality, TV, and education, effectively allocating the resources of next-generation wireless technologies for MVV streams becomes increasingly crucial. To achieve high utility for MVV users, this paper proposes a cross-layer resource allocation mechanism to leverage video synthesizing schemes (such as Depth-Image-Based Rendering (DIBR) for efficient MVV streaming with massive MIMO). First, we formulate a new problem, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">antenna allocation with video synthesis</i> (AAVS), and prove its NP-hardness. Then, we design an approximation algorithm named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Utility-based Multi-View Synthesis</i> (UMVS) with the analytical performance provided, and dynamic scenarios are addressed by augmenting UMVS with deep reinforcement learning. Data-driven simulation results show that UMVSoutperforms existing antenna allocation schemes by at least 10%, and the DRL extension provides an additional 6% improvement in system utility under congested scenarios.
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