Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

TL;DR

This paper introduces a semantic communication framework using contest theory and deep reinforcement learning to optimize resource allocation for avatar synchronization in the Metaverse, significantly reducing network load and improving user experience.

Contribution

It presents a novel semantic communication approach combined with contest theory and Deep Q-Networks for efficient resource allocation in Metaverse avatar synchronization.

Findings

Semantic data size reduced to 51 bytes from 8.243 MB.

66.076% reduction in down-sampling loss with optimal reward setting.

Enhanced resource allocation improves user experience in VR environments.

Abstract

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076\% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.