| Paper ID | IVMSP-16.2 |
| Paper Title |
QOE-DRIVEN AND TILE-BASED ADAPTIVE STREAMING FOR POINT CLOUDS |
| Authors |
Lisha Wang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong, Shanghai Jiao Tong University, China |
| Session | IVMSP-16: Point Clouds and Depth |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVCOM] Image & Video Communications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Application of point clouds is in critical demand, which, however, are composed of large amounts of data and difficult to stream in bandwidth-constrained networks. To address this, we propose a QoE-driven and tile-based adaptive streaming approach for point clouds, to reduce transmission redundancy and maximize user's QoE. Specifically, by utilizing the perspective projection, we model the QoE of a 3D tile as a function of the bitrate of its representation, user's view frustum and spatial position, occlusion between tiles, and the resolution of rendering device. We then formulate the QoE-optimized rate adaptation problem as a multiple-choice knapsack problem that allocates bitrates for different tiles under a given transmission capacity. We equivalently convert it as a submodular function maximization problem subject to knapsack constraints, and develop a practical greedy algorithm with a theoretical performance guarantee. Experimental results further demonstrate superiority of the proposed rate adaptation algorithm over existing schemes, in terms of both user's visual quality and transmission efficiency. |
