Multi-codec rate adaptive point cloud streaming for holographic-type communication

Abstract

Point cloud videos play a crucial role in immersive applications enabled by holographic-type communication, which has been identified as an important service for 6G and beyond wireless systems and the metaverse. The significant volume of point cloud video demands efficient compression and transmission techniques to support the Quality of Experience (QoE) requirements of real-time immersive applications. A few Point Cloud Compression (PCC) techniques, such as MPEG PCC and Draco, have emerged in recent years, and studies have shown that each technique has its strengths and weaknesses under different system settings. This paper proposes a multi-codec rate adaptive point cloud streaming method to satisfy the QoE requirements of interactive and live applications considering available system resources. The proposed method leverages three common PCC techniques: MPEG V-PCC, MPEG G-PCC, and Draco. The performance of each PCC technique is evaluated under various test conditions, and then estimation models are constructed to predict the bit rate, the decoding time, and the quality of the reconstructed point cloud. Based on the user's quality requirements and available computational and communication resources, the proposed streaming method selects a codec along with appropriate compression parameters that can provide the minimum latency for streaming. Evaluation results demonstrate that the proposed method can provide better QoE than benchmark methods under various bandwidth and computation scenarios.