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 substantially extend 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.

Task offloading has become a key term in the field of immersive media technologies: it can enable lighter and cheaper devices while providing them higher remote computational capabilities. In this paper we present our TCP-based offloading architecture. The architecture, has been specifically designed for immersive media offloading tasks with a particular care in reducing any processing overhead which can degrade the network performance. We tested the architecture for different offloading scenarios and conditions on two different wireless networks: WiFi and 5G millimeter wave technologies. Besides, to test the network on alternative millimeter wave configurations, currently not available on the actual 5G millimeter rollouts, we used a 5G Radio Access Network (RAN) real-time emulator. This emulator was also used to test the offloading architecture for an simulated immersive user sharing network resources with other users. We provide insights of the importance of user prioritization techniques for successful immersive media offloading. The results show a great performance for the tested immersive media scenarios, highlighting the relevance of millimeter wave technology for the future of immersive media applications.

In this paper, a method for online parameter estimation and automatic control of a system of rigidly attached quadcopters is introduced. First, the method performs an estimation of the physical structure attaching the quadcopters by relying solely on information from the quadcopters' Inertial Measurement Units (IMU). This information is obtained via simple and short online experiments, allowing their plug and play assembly without any human intervention. Then, given the estimated physical attachment's parameters, a stable operation of the quadcopters is achieved via an adaptive controller architecture, where the controller parameters are obtained using Reinforcement Learning. Finally, experimental results validate the proposed method, showing that a correct estimation of the physical structure is obtained allowing the autonomous flight of a pair of attached quadcopters.