Information Sharing in Multi-Tenant Metaverse via Intent-Driven Multicasting

Abstract

A multi-tenant metaverse enables multiple users in a common virtual world to interact with each other online. Information sharing will occur when interactions between a user and the environment are multicast to other users by an interactive metaverse (IM) service. However, ineffective information-sharing strategies intensify competitions among users for limited resources in networks, and fail to interpret optimization intent prompts conveyed in high-level natural languages, ultimately diminishing user immersion. In this paper, we explore reliable information sharing in a multi-tenant metaverse with time-varying resource capacities and costs, where IM services are unreliable and alter the volumes of data processed by them, while the service provider dynamically adjusts global intent to minimize multicast delays and costs. To this end, we first formulate the information sharing problem as a Markov decision process and show its NP-hardness. Then, we propose a learning-based system GTP, which combines the proximal policy optimization reinforcement learning with feature extraction networks, including graph attention network and gated recurrent unit, and a Transformer encoder for multi-feature comparison to process a sequence of incoming multicast requests without the knowledge of future arrival information. The GTP operates through three modules: a deployer that allocates primary and backup IM services across the network to minimize a weighted goal of server computation costs and communication distances between users and services, an intent extractor that dynamically infers provider intent conveyed in natural language, and a router that constructs on-demand multicast routing trees adhering to users, the provider, and network constraints. We finally conduct theoretical and empirical analysis on the proposed algorithms for the system. Experimental results show that the proposed algorithms are promising, and superior to their comparison baseline algorithms.