# Edge Rendering Architecture for multiuser XR Experiences and E2E Performance Assessment

arXiv: https://arxiv.org/abs/2406.07087 · HTML: https://mlsanity.com/paper/2406.07087/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines](https://mlsanity.com/paper/2603.16823/) (2026)

## Abstract (arXiv)

Holographic communications are gaining ground among emerging eXtended-Reality (XR) applications due to their potential to revolutionize human communication. However, these technologies are characterized by higher requirements in terms of Quality of Service (QoS), such as high transmission data rates, very low latency, and high computation capacity, challenging current achievable capabilities. In this context, computation offloading techniques are being investigated, where resource-intensive computational tasks, like rendering XR experiences, are shifted from user devices to a separate processor, specifically an Edge Computing instance. This paper introduces an Edge Rendering architecture for multiuser XR experiences, implements it on top of widely employed XR and Web technologies, and proposes a method based on image and audio processing to evaluate its performance in terms of end-to-end media streaming latency, inter-device, and intra-media synchronization when employing different access networks.