Cell Configuration for QoE-Aware Volumetric Video Streaming via Hierarchical Reinforcement Learning

Volumetric video provides immersive experiences, but it requires extremely high bandwidth to support real-time streaming. Cell-based streaming has emerged as an effective solution by culling out invisible cells and transmitting necessary content. Cell size has a significant influence on culling performance and compression efficiency. Meanwhile, the optimal cell size varies with dynamic viewports and bandwidth conditions. Therefore, it is critical to adjust cell size in real time instead of using a fixed cell size. Additionally, cell bitrate allocation plays a critical role in volumetric video streaming, as it determines the quality of each cell. It is influenced by the cell size, which directly changes the number of cells, cell spatial importance, and compression efficiency. To address the cell size and bitrate configuration, a hierarchical reinforcement learning framework is proposed to dynamically optimize these decisions, aiming to maximize quality of experience. This novel framework is evaluated through trace-driven simulations. Results demonstrate that the proposed approach effectively optimizes cell size and bitrate allocation. It significantly improves QoE compared to existing video streaming schemes with the fixed cell size, across diverse network conditions, video sources, and user behaviors.