Metaverse study

• Main Author: Nguyen, Donvis
• Other Authors: Jun Zhao
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/166805
• Institution: Nanyang Technological University

Computer vision (CV) and machine learning plays a big role in the development of Virtual Reality (VR) experiences for users. In the Metaverse, users often use VR devices such as headsets and motion capture devices to enhance the immersion of such digital spaces. However, computer vision and machine learning are computationally intensive tasks given the amount of data required to be processed in real time. Given the limited computing power and battery life of wireless VR devices, these devices are inefficient in handling some of these tasks in real-time. However, with the development of mobileedge computing (MEC), it may serve as a solution to distribute computational resources across networks, to improve the data processing capability of the wireless VR headsets. In this paper, we explore a deep reinforcement learning algorithm to offload the computationally and resource-intensive tasks to MEC servers to reduce the latency and improve the performance of these VR devices, hoping to make connections and interactions to the Metaverse a better experience for users.