Toward Optimal Real-time Dynamic Point Cloud Streaming over Bandwidth-constrained Networks
Nguyen Long Quang, Duc V. Nguyen, Trương Thu Hương · 2023
Point cloud is the emerging format for representing real-world objects in VR/AR applications. However, real-time streaming of dynamic point clouds presents challenges due to high data rates and low latency requirements. This paper introduces a novel and bandwidth-efficient streaming approach for scenes consisting of multiple dynamic point clouds over networks with limited bandwidth. The proposed approach dynamically adjusts the Level of Detail (LoD) of individual point clouds based on network conditions and user preferences to optimize the user’s Quality of Experience (QoE). The LoD version selection problem is formulated as a QoE optimization problem, and two real-time solutions are presented for deciding the LoD version for each point cloud. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of visual quality while achieving remarkably low processing time, about 0.01 ms. These findings have the potential to advance seamless user experience.