A Deep Reinforcement Learning Approach for Point Cloud Video Transmissions

The point cloud videos, thanks to the multi-view and immersive experiences, have recently attracted notable attentions from both academia and industry. Due to the high data volume, a point cloud video also raises the challenge of quality-of-experience (QoE), which is in terms of the balance between playback quality and buffering delay during the transmission under time-varying system conditions. In this paper, we propose a deep reinforcement learning (DRL) approach to optimize the expected long-term QoE for the client. Over the time horizon, the proposed approach learns to select the tiles of the corresponding video for transmissions in an iterative way. Under various settings, numerical experiments based on real throughput data traces are conducted to evaluate the proposed approach. Compared to the baselines, our approach not only enhances the video quality but also reduces the re-buffering time, obtaining an improvement of average QoE for the client by 9%-14%.