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arXiv AI By Yoga Suhas Kuruba Manjunath, Jie Gao, Lian Zhao

ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended Reality

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ResLearn-XR is a residual learning framework designed to predict extended reality (XR) network traffic and estimate Quality-of-Experience (QoE) risk. It uses a two‑stage temporal learning structure: a base sequence prediction model followed by task‑specific residual components that operate in value space for traffic forecasting and in logit space for probabilistic QoE risk estimation. The framework introduces a Data Descriptor Algorithm (DDA) to convert packet‑level observables into frame‑timing‑aware descriptors and is evaluated on a newly constructed XR Traffic‑QoE dataset, achieving significant reductions in SMAPE for both traffic prediction and QoE‑risk estimation compared to single‑stage baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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