Coarse-Grained Predictive Resource Allocation Framework enabling In-Premise XR Collaborations

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.