# ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction

arXiv: https://arxiv.org/abs/2411.11894 · HTML: https://mlsanity.com/paper/2411.11894/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Virtual Reality Gaming on the Cloud: A Reality Check](https://mlsanity.com/paper/2109.10114/) (2021)
- [User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality](https://mlsanity.com/paper/2311.10645/) (2023)
- [Time-Distributed Feature Learning for Internet of Things Network Traffic Classification](https://mlsanity.com/paper/2409.05096/) (2024)

## Built on by (most cited first, 1 of 1)

- [ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended Reality](https://mlsanity.com/paper/2609.04493/) (2026)

## Abstract (arXiv)

Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) services. We first introduce a state-of-the-art testbed capturing a real-world dataset of virtual reality (VR), augmented reality (AR), and mixed reality (MR) traffic, made openly available for further research. To enhance prediction accuracy, we then propose a novel view-frame (VF) algorithm that accurately identifies video frames from traffic while ensuring privacy compliance, and we develop a Transformer-based progressive error-learning algorithm, referred to as ResLearn for Metaverse traffic prediction. ResLearn significantly improves time-series predictions by using fully connected neural networks to reduce errors, particularly during peak traffic, outperforming prior work by 99%. Our contributions offer Internet service providers (ISPs) robust tools for real-time network management to satisfy Quality of Service (QoS) and enhance user experience in the Metaverse.