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

arXiv: https://arxiv.org/abs/2609.04493 · HTML: https://mlsanity.com/paper/2609.04493/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Estimating Expected Calibration Errors](https://mlsanity.com/paper/2109.03480/) (2021)
- [Towards Low-Latency and Ultra-Reliable Virtual Reality](https://mlsanity.com/paper/1801.07587/) (2018)
- [Virtual Reality Gaming on the Cloud: A Reality Check](https://mlsanity.com/paper/2109.10114/) (2021)
- [Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality](https://mlsanity.com/paper/2305.08569/) (2023)
- [Stratification of uncertainties recalibrated by isotonic regression and its impact on calibration error statistics](https://mlsanity.com/paper/2306.05180/) (2023)
- [An eXtended Reality Offloading IP Traffic Dataset and Models](https://mlsanity.com/paper/2301.11217/) (2023)
- [An Open Framework for Analyzing and Modeling XR Network Traffic](https://mlsanity.com/paper/2108.04577/) (2021)
- [ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction](https://mlsanity.com/paper/2411.11894/) (2024)

## Abstract (arXiv)

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. revFor 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.