Point cloud video streaming over networks is challenging because of the extremely high data rate of uncompressed point cloud data. Adaptive point cloud video streaming has been proposed to deal with this challenge. However, temporal quality variation and stalling might occur under unstable network conditions, potentially degrading users' Quality of Experience (QoE). This paper aims to evaluate and model the impacts of temporal quality variations and stalling on users' QoE in adaptive point cloud video streaming. We first conduct a largescale subjective study to construct a QoE database. Then, based on the constructed database, the effects of individual factors are analyzed, and two novel QoE prediction models are presented. Experiment results show that the proposed QoE models achieve high prediction performance in terms of PLCC, SROCC, and RMSE across various point cloud videos.

In this paper, we develop a Virtual Reality-based immersive learning environment that allows teachers to conduct a lesson in a virtual space. The proposed system allows teachers and students to be present in the same virtual space regardless of their actual physical locations. The teachers can verbally communicate with students in real-time, sharing/collecting learning materials, and conducting in-class assignments. The students can interactively learn about real-world objects by touching and manipulating 3D models with hand tracking. Evaluation results demonstrate the effectiveness of the proposed system.

Point cloud video is an effective method to represent moving objects for metaverse applications. Real-time streaming of point cloud video can offer truly immersive experiments for metaverse users. However, point cloud videos have an extremely high data rate and demand significant processing resources for compression and rendering at the user device. To address the above challenges, this paper presents a novel server-driven viewaware point cloud video streaming system that can effectively reduce bandwidth requirements by combining hidden point removal and video-based point cloud encoding. Experiment results show that the proposed method can reduce the bandwidth requirement by up to 24% compared to the baseline method.