Cross-Layer Video Synthesizing and Antenna Allocation Scheme for Multi-View Video Provisioning Under Massive MIMO Networks

Yishuo Shi, Wen-Hsing Kuo, Chih-Wei Huang, Yen-Cheng Chou, Shih-Hau Fang, De-Nian Yang

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 article 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, antenna allocation with video synthesis (AAVS), and prove its NP-hardness. Then, we design an approximation algorithm named Utility-based Multi-View Synthesis (UMVS) with the analytical performance provided, and dynamic scenarios are addressed by augmenting UMVS with deep reinforcement learning. Data-driven simulation results show that UMVS outperforms existing antenna allocation schemes by at least 10%, and the DRL extension provides an additional 6% improvement in system utility under congested scenarios.