The telecommunications industry is shifting toward an experience-driven era defined by immersive applications that impose stringent network demands. While passive optical network (PON)-based Fiber-To-The-Home (FTTH) architectures have historically delivered high-capacity broadband, a growing performance gap between access and indoor networks necessitates driving fiber deeper into premises. Complementing existing copper and WiFi technologies, Fiber-to-the-Room (FTTR) is emerging as a critical enabler. This article examines the drivers of this architectural shift, specifically focusing on collaborative extended reality (XR). We analyze the multi-modal traffic characteristics and Quality-of-Experience (QoE) requirements of XR, alongside the resulting network performance implications. To address these demands, we propose a novel resource allocation framework integrating coarse-grained traffic forecasting with intelligent in-premise roaming. This framework facilitates the flexible deployment of artificial intelligence models tailored to the computational capabilities of the FTTH, FTTR, and WiFi segments. Numerical evaluations of a representative multi-tier network demonstrate that long-horizon, multi-step-ahead traffic forecasts significantly reduce inference frequency. This approach enables coordinated resource allocation and network-orchestrated seamless mobility across rooms, effectively satisfying collaborative XR QoE requirements. Finally, we highlight strategies to amortize FTTR capital expenditure by extending the optical infrastructure's role beyond basic connectivity. By supporting diverse services over a unified physical layer, FTTR enhances infrastructure utilization, unlocks novel revenue streams, and maximizes the value of in-premise fiber deployments.

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