Problem
Research questions and friction points this paper is trying to address.
Metaverse
Extended Reality (XR)
Content Transmission
Network Bottleneck
Generative AI
Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework
🤖 AI Summary
This study addresses the network bandwidth bottleneck in metaverse extended reality (XR) content delivery by proposing an intelligent cloud-edge collaborative transmission architecture. The framework leverages generative artificial intelligence at the network edge to reconstruct missing XR content, thereby alleviating transmission pressure. Furthermore, a deep reinforcement learning-based decision module is designed to jointly optimize content generation and transmission scheduling. Experimental results demonstrate that, compared with baseline methods, the proposed approach increases the proportion of valid frames satisfying quality-of-service and latency constraints by 2.8 times, substantially enhancing the overall XR streaming experience.
📝 Abstract
How to efficiently transmit large volumes of Extended Reality (XR) content through current networks has been a major bottleneck in realizing the Metaverse. The recently emerging Generative Artificial Intelligence (GAI) has already revolutionized various technological fields and provides promising solutions to this challenge. In this article, we first demonstrate current networks’ bottlenecks for supporting XR content transmission in the Metaverse. Then, we explore the potential approaches and challenges of utilizing GAI to overcome these bottlenecks. To address these challenges, we propose a GAI-based XR content transmission framework which leverages a cloud-edge collaboration architecture. The cloud servers are responsible for storing and rendering the original XR content, while edge servers utilize GAI models to generate essential parts of XR content (e.g., subsequent frames, selected objects, etc.) when network resources are insufficient to transmit them. A Deep Reinforcement Learning (DRL)-based decision module is proposed to solve the decision-making problems. Our case study demonstrates that the proposed GAI-based transmission framework achieves a 2.8-fold increase in normal frame ratio (percentage of frames that meet the quality and latency requirements for XR content transmission) over baseline approaches, underscoring the potential of GAI models to facilitate XR content transmission in the Metaverse.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative AI
Extended Reality (XR)
Cloud-Edge Collaboration
Deep Reinforcement Learning (DRL)
Metaverse
🔎 Similar Papers
No similar papers found.
