# An eXtended Reality Offloading IP Traffic Dataset and Models

arXiv: https://arxiv.org/abs/2301.11217 · HTML: https://mlsanity.com/paper/2301.11217/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2023.

## Built on by (most cited first, 3 of 3)

- [Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application](https://mlsanity.com/paper/2608.05346/) (2026)
- [Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks](https://mlsanity.com/paper/2608.05340/) (2026)
- [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)

In recent years, advances in immersive multimedia technologies, such as extended reality (XR) technologies, have led to more realistic and user-friendly devices. However, these devices are often bulky and uncomfortable, still requiring tether connectivity for demanding applications. The deployment of the fifth generation of telecommunications technologies (5G) has set the basis for XR offloading solutions with the goal of enabling lighter and fully wearable XR devices. In this paper, we present a traffic dataset for two demanding XR offloading scenarios that are complementary to those available in the current state of the art, captured using a fully developed end-to-end XR offloading solution. We also propose a set of accurate traffic models for the proposed scenarios based on the captured data, accompanied by a simple and consistent method to generate synthetic data from the fitted models. Finally, using an open-source 5G radio access network (RAN) emulator, we validate the models both at the application and resource allocation layers. Overall, this work aims to provide a valuable contribution to the field with data and tools for designing, testing, improving, and extending XR offloading solutions in academia and industry.