AHCI RESEARCH GROUP

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Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Zhang, Z.; Jiang, Y.; Wei, X.; Chen, M.; Dong, H.; Yu, S.

Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework Journal Article

In: IEEE Network, 2025, ISSN: 08908044 (ISSN).

Abstract | Links | BibTeX | Tags: 'current, Cloud servers, Collaboration architecture, Content transmission, Decision making, Deep learning, Deep reinforcement learning, Edge server, Generative adversarial networks, Intelligence models, Large volumes, Metaverses, Network bottlenecks, Reinforcement Learning, Through current

@article{zhang_generative-ai_2025,

title = {Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework},

author = {Z. Zhang and Y. Jiang and X. Wei and M. Chen and H. Dong and S. Yu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-86000661262&doi=10.1109%2fMNET.2025.3547385&partnerID=40&md5=1e00d40542ec58ef1489934abb2a990c},

doi = {10.1109/MNET.2025.3547385},

issn = {08908044 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Network},

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. © 2025 IEEE.},

keywords = {'current, Cloud servers, Collaboration architecture, Content transmission, Decision making, Deep learning, Deep reinforcement learning, Edge server, Generative adversarial networks, Intelligence models, Large volumes, Metaverses, Network bottlenecks, Reinforcement Learning, Through current},

pubstate = {published},

tppubtype = {article}

}