Predictive resource allocation improves extended reality network quality
Coarse-Grained Predictive Resource Allocation Framework enabling In-Premise XR Collaborations
Networking and Internet Architecture
Summary
Extended reality (XR) applications, like virtual and augmented reality, need very fast and reliable internet connections to work well without making users feel sick. The authors designed a system that predicts future network traffic to better manage resources across different types of home and room networks, including fiber optic and WiFi. They used advanced AI models tailored to each network part, improving prediction accuracy and allowing smoother movement of XR users between rooms. This approach helps keep XR experiences smooth and responsive even when wireless connections change unpredictably.
What this means in practice
- •For network schedulers: Improve multi-tier indoor network resource use by forecasting XR traffic for better bandwidth and latency management.
- •For wireless network operators: Enable seamless room-to-room XR device handoffs by coordinating fiber and WiFi resources based on long-term traffic predictions.
Authors
Sourav Mondal, Elaine Wong
Abstract
Immersive extended reality (XR) applications are key enablers of human-centric connectivity in next-generation networks. However, meeting their high-throughput, ultra-low-latency, and high-reliability demands while preventing cyber-sickness remains a critical challenge, especially in environments with non-deterministic wireless connectivity. Fiber-To-The-Room (FTTR), extending widely deployed Fiber-To-The-Home (FTTH) infrastructure, offers a promising solution. Yet, satisfying the stringent Quality-of-Experience (QoE) requirements of XR collaborators demands robust resource allocation across the multi-tier cascade of FTTH, FTTR, and WiFi segments. In this paper, we propose a coarse-grained traffic forecasting-empowered proactive resource allocation and intelligent in-premise roaming framework. Here, artificial intelligence (AI) model complexities are scaled to the computational capabilities of each network segment. At the FTTR segment, we employ a hybrid temporal convolutional and bi-directional long short-term memory (TCN-BiLSTM) network for multi-modal XR traffic forecasting, integrating passive optical network (PON) and WiFi access points with multi-link operation (MLO) and orthogonal frequency-division multiple access (OFDMA). The resulting long-horizon forecasts reduce inference frequency, enabling coordinated FTTR-WiFi resource allocation and seamless network-orchestrated mobility across rooms. At the FTTH segment, we deploy Transformer-based time-series models (e.g., Informer, Autoformer, PatchTST, and TFT) to predict the complex XR traffic aggregated in FTTR segments for transmission over the FTTH backhaul. Evaluations show Transformer-based models are up to $\sim$75% more accurate in long-horizon forecasting than the hybrid model. Extensive simulations confirm the proposed framework significantly outperforms traditional methods in satisfying XR QoE requirements.