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 · IEEE Transactions on Mobile Computing · 2022
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 namedUtility-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.