# QoE-Aware Service Provision for Mobile AR Rendering: An Agent-Driven Approach

arXiv: https://arxiv.org/abs/2508.08627 · HTML: https://mlsanity.com/paper/2508.08627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [NetLLM: Adapting Large Language Models for Networking](https://mlsanity.com/paper/2402.02338/) (2024)
- [Digital Twin-based 3D Map Management for Edge-assisted Device Pose Tracking in Mobile AR](https://mlsanity.com/paper/2311.04997/) (2023)
- [AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization](https://mlsanity.com/paper/2301.04620/) (2023)
- [Exploring the 6G Potentials: Immersive, Hyper Reliable, and Low-Latency Communication](https://mlsanity.com/paper/2407.11051/) (2024)
- [User-centric Immersive Communications in 6G: A Data-oriented Framework via Digital Twin](https://mlsanity.com/paper/2410.02688/) (2024)
- [Quality of Experience Oriented Cross-layer Optimization for Real-time XR Video Transmission](https://mlsanity.com/paper/2404.09905/) (2024)
- [QoE-oriented Communication Service Provision for Annotation Rendering in Mobile Augmented Reality](https://mlsanity.com/paper/2501.07127/) (2025)

## Abstract (arXiv)

Mobile augmented reality (MAR) is envisioned as a key immersive application in 6G, enabling virtual content rendering aligned with the physical environment through device pose estimation. In this paper, we propose a novel agent-driven communication service provisioning approach for edge-assisted MAR, aiming to reduce communication overhead between MAR devices and the edge server while ensuring the quality of experience (QoE). First, to address the inaccessibility of MAR application-specific information to the network controller, we establish a digital agent powered by large language models (LLMs) on behalf of the MAR service provider, bridging the data and function gap between the MAR service and network domains. Second, to cope with the user-dependent and dynamic nature of data traffic patterns for individual devices, we develop a user-level QoE modeling method that captures the relationship between communication resource demands and perceived user QoE, enabling personalized, agent-driven communication resource management. Trace-driven simulation results demonstrate that the proposed approach outperforms conventional LLM-based QoE-aware service provisioning methods in both user-level QoE modeling accuracy and communication resource efficiency.