Jie Li
ABSTRACT
As a popular way of representing holographic video or volumetric video, point cloud video can provide users with a highly immersive viewing experience of 6 degrees of freedom (6DoF) and is expected to become the mainstream video format of the future. However, the real-time transmission of point cloud video faces many challenges due to the huge amount of data and the large search space of the optimization problem with constraints. To this end, we propose Horizon, a novel Dynamic Adaptive Streaming over HTTP (DASH) based real-time point cloud video streaming system, which aims to maximize the user's viewing experience by predicting the next several steps through a rolling framework and uses a Deep Reinforcement Learning (DRL) based algorithm to achieve a real-time solution to the rolling optimization problem. We have prototyped this system and demonstrated its performance on a state-of-the-art wireless network.
- Eugene d'Eon, Bob Harrison, Taos Myers, and Philip A Chou. 2017. 8i voxelized full bodies-a voxelized point cloud dataset. ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG) input document WG11M40059/WG1M74006 7, 8 (2017), 11.Google Scholar
- Jie Li, Cong Zhang, Zhi Liu, Richang Hong, and Han Hu. 2022. Optimal Volumetric Video Streaming with Hybrid Saliency based Tiling. IEEE Transactions on Multimedia (2022), 1--1.Google ScholarDigital Library
- Zhi Liu, Qiyue Li, Xianfu Chen, Celimuge Wu, Susumu Ishihara, Jie Li, and Yusheng Ji. 2021. Point Cloud Video Streaming: Challenges and Solutions. IEEE Network 35, 5 (2021), 202--209.Google ScholarDigital Library
- Li Yu, Tammam Tillo, and Jimin Xiao. 2017. QoE-Driven Dynamic Adaptive Video Streaming Strategy With Future Information. IEEE Transactions on Broadcasting 63, 3 (2017), 523--534.Google ScholarCross Ref
Index Terms
- Demo: Horizon: a Real-time Point Cloud Video Streaming System over Wireless Networks
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