AI-assisted Improved Service Provisioning for Low-latency XR over 5G NR

TL;DR

This paper introduces an AI-assisted scheme for XR over 5G that uses predicted frames to enhance service provisioning, significantly increasing supported users despite minor prediction errors.

Contribution

It proposes a novel AI-based approach leveraging predicted frames to improve low-latency XR service provisioning over 5G networks.

Findings

Multi-fold increase in supported XR users

Effective virtual delay budget extension

Insights into network design for XR

Abstract

Extended Reality (XR) is one of the most important 5G/6G media applications that will fundamentally transform human interactions. However, ensuring low latency, high data rate, and reliability to support XR services poses significant challenges. This letter presents a novel AI-assisted service provisioning scheme that leverages predicted frames for processing rather than relying solely on actual frames. This method virtually increases the network delay budget and consequently improves service provisioning, albeit at the expense of minor prediction errors. The proposed scheme is validated by extensive simulations demonstrating a multi-fold increase in supported XR users and also provides crucial network design insights.