Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning

TL;DR

This paper introduces ASMS, a federated multi-agent deep reinforcement learning system that dynamically optimizes streaming quality in the social metaverse, balancing privacy, low latency, and high user experience.

Contribution

It proposes a novel federated multi-agent DRL approach, F-MAPPO, for adaptive streaming that preserves user privacy while improving streaming quality in the social metaverse.

Findings

ASMS improves user experience by at least 14% over existing methods.

The system effectively balances privacy and streaming quality.

ASMS performs well across various network conditions.

Abstract

The social metaverse is a growing digital ecosystem that blends virtual and physical worlds. It allows users to interact socially, work, shop, and enjoy entertainment. However, privacy remains a major challenge, as immersive interactions require continuous collection of biometric and behavioral data. At the same time, ensuring high-quality, low-latency streaming is difficult due to the demands of real-time interaction, immersive rendering, and bandwidth optimization. To address these issues, we propose ASMS (Adaptive Social Metaverse Streaming), a novel streaming system based on Federated Multi-Agent Proximal Policy Optimization (F-MAPPO). ASMS leverages F-MAPPO, which integrates federated learning (FL) and deep reinforcement learning (DRL) to dynamically adjust streaming bit rates while preserving user privacy. Experimental results show that ASMS improves user experience by at least 14% compared to existing streaming methods across various network conditions. Therefore, ASMS enhances the social metaverse experience by providing seamless and immersive streaming, even in dynamic and resource-constrained networks, while ensuring that sensitive user data remains on local devices.