Toward 6G-Based Metaverse: Supporting Highly-Dynamic Deterministic Multi-User Extended Reality Services
Authors/Creators
Description
Metaverse is the concept of a fully immersive and universal virtual space for multiuser interaction, collaboration, and socializing; forming the next evolution of the Internet. Metaverse depends on the convergence of multiple broad technologies that enable eXtended Reality (XR), which is an umbrella term for technologies that lie on the reality-virtuality continuum, namely Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Compared to streaming volumetric content to a single user, the XR applications that involve multiple users who simultaneously watch the same volumetric content (e.g., VR-based online training/ education and multi-user online gaming) are much more attractive. Multi-user XR/Metaverse puts additional demand on the underlying networks, making it more difficult to offer high-quality immersive material in real-time. To cope with this, in this article, we present a comprehensive system and component design for immersive and seamless multi-user XR experiences. To satisfy the Quality of Experience/Quality of Service (QoE/ QoS) requirements and especially stream synchronization requirement in XR collaboration scenarios, we propose an AI-powered deterministic multi-user extended reality resource orchestrator (PRECISENESS) that aims to solve the multi-user XR service provisioning problem. Finally, we demonstrate the performance of our proposed solution in a single-site multi-user XR use case. The obtained results demonstrate that our solution can deliver high-quality immersive XR services.
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Additional details
Funding
- European Commission
- 6G-SANDBOX - Supporting Architectural and technological Network evolutions through an intelligent, secureD and twinning enaBled Open eXperimentation facility 101096328
- Business Finland
- 6Bridge 6Core 8410/31/2022
- Research Council of Finland
- 6G Flagship - 6G-Enabled Wireless Smart Society & Ecosystem 346208
- Research Council of Finland
- Industrial Distributed Edge Architecture over Machine Intelligence for Local Learning 352428