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Life sciences · Preprint
arXiv · September 3, 2026
Posted before peer review. The findings may change or fail to hold.
This is a preprint describing ResLearn-XR, a machine-learning framework for predicting extended-reality network traffic and estimating quality-of-experience risk. The work is computational and does not address clinical efficacy, patient safety, or healthcare outcomes; it is not applicable to clinical practice guidance.
Preprint.
ResLearn-XR reduces SMAPE by up to 17.84% across frame-count, frame-size, and inter-arrival-time prediction compared to baseline models. QoE-risk estimation SMAPE reduced by up to 87.8% over single-stage baselines using the proposed residual learning approach. A Data Descriptor Algorithm (DDA) module converts packet-level observables into frame-timing-aware descriptors for encrypted traffic analysis.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is an unrefereed technical preprint describing a machine-learning framework for network traffic prediction; it reports no clinical outcomes, patient data, or evidence relevant to clinical practice.
Quoted from the source exactly as published.
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We present ResLearn-XR, a residual learning framework for predicting eXtended Reality (XR) network traffic and estimating Quality-of-Experience (QoE) risk. ResLearn-XR adopts a two-stage temporal learning structure comprising a base sequence prediction model augmented with task-specific residual learning components to improve adaptability to bursty, non-stationary XR traffic dynamics. The residual learning stages operate in the value space for continuous XR traffic forecasting and in the logit space for probabilistic QoE risk estimation. \rev{For the QoE-risk branch, we introduce a Data Descriptor Algorithm (DDA), a causal feature-construction module that converts packet-level application-layer observables into frame-timing-aware descriptors suitable for encrypted traffic analysis. We also construct an XR Traffic-QoE dataset that pairs continuous XR traffic traces with session-level user-reported QoE labels. ResLearn-XR reduces SMAPE by up to 17.84% across frame-count, frame-size, and inter-arrival-time prediction, while reducing QoE-risk estimation SMAPE by up to 87.8% over single-stage baselines.
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