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.

翻译：近年来，沉浸式多媒体技术（如扩展现实技术）的进步推动了更真实、更便捷的设备发展。然而，这些设备通常体积庞大且佩戴不适，在运行高要求应用时仍需依赖有线连接。第五代通信技术（5G）的部署为XR卸载解决方案奠定了基础，旨在实现更轻量级、可完全穿戴的XR设备。本文针对当前研究领域中尚属空缺的两种高要求XR卸载场景，通过一套完整的端到端XR卸载解决方案捕获流量数据，并提出基于该数据的高精度流量模型。同时，我们提供一种简单且一致的方法，用于从拟合模型中生成合成数据。最后，利用开源5G无线接入网（RAN）模拟器，在应用层和资源分配层对模型进行验证。总体而言，本研究旨在通过提供数据与工具，为学术界和工业界设计、测试、改进及扩展XR卸载方案做出重要贡献。