AITransfer: Progressive AI-powered Transmission for Real-Time Point Cloud Video Streaming

Yakun Huang, Yuanwei Zhu, Xiuquan Qiao, Zhijie Tan, Boyuan Bai

Abstract: Point cloud video provides a more immersive holographic virtual experience than conventional video services such as 360 degree video and virtual reality (VR) video. However, the existing network bandwidth and transmission technology can not carry real-time point cloud video streaming due to mass data volume, high processing overheads, and extremely bandwidth-consuming. Unlike previous approaches that extend the VR video streaming, we propose AITransfer, an AI-powered bandwidth-aware and adaptive transmission technique driven by extracting and transferring key point cloud features to reduce the bandwidth consumption and alleviate the computational pressure. AITransfer has two outstanding contributions, including (1) incorporating the dynamic network bandwidth into the design of an end-to-end architecture with two fundamental contents of feature extraction and reconstruction, and (2) employing an online adapter to sense the network bandwidth and match the optimal inference model. We conduct extensive experiments on the typical dataset and develop a case study to demonstrate the efficiency and effectiveness. The results show that AITransfer can provide more than 30.72 times compression ratio under the existing network environments.