ML-based network management framework for XR services.

This work presents a novel framework designed for the management of XR (Extended Reality) services for B5G/6G network paradigms. These networks will enable its near-future deployment to change the concept of the XR experiences known at this moment. Our proposed framework powered by ML (Machine Learning) consists of the measurement and estimation of metrics based on network-accessible information, and a proof of concept of network optimization. The latter is based on the use of KQI (Key Quality Indicators) to tune the performance of XR services. This in conjunction with ML approaches, can offer additional levels of intelligence to networks. To validate this, a 360-video service has been selected as a use case to provide a proof of concept of the performance, utility, and novelty of this work.This work has been partially funded by: Ministerio de Asuntos Económicos y Transformación Digital and European Union - NextGenerationEU within the framework “Recuperación, Transformación y Resiliencia y el Mecanismo de Recuperación y Resiliencia” under the project MAORI, and Universidad de Málaga through the “II Plan Propio de Investigación, Transferencia y Divulgación Científica”. This work has been also supported by Junta de Andalucía through Secretaría General de Universidades, Investigación y Tecnología with predoctoral grant (Ref. PREDOC_01712) as well as by Ministerio de Ciencia y Tecnología through grant FPU19/04468. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Adquisición de métricas para el servicio de Vídeo 360/VR

Virtual Reality (VR) arises as one of the current cutting-edge technologies. Its applications address educational and entertainment uses. This work presents a framework to assess video 360 service performance over VR headsets through Key Quality Indicators (KQIs). This service differs from traditional video streaming approaches due to its immersive experience, which allows the user to enjoy omnidirectional multimedia. However, the service experience must be guaranteed in order to avoid side effects such as cybersickness or disorientation. The testbed is conformed by a Unity video player, which reproduce multimedia sources (DASH and HLS) from a video server located in the cloud while KQI measuring tasks are performed. Finally, a performance comparison between technologies is provided. Results from the KQI measurement highlight the potential of the new generation of mobile networks in the provision of service with high-quality levels of experience.Este trabajo ha sido parcialmente financiado por la Junta de Andalucía y el EDRF en el marco del proyecto AECMA-5G: Advanced E2E Cellular Management for 5G Applications (Ref. UMA-CEIATECH-14, “Proyecto singular de actuaciones de transferencia del conocimiento Campus Excelencia Internacional Andalucía TECH. Ecosistema innovador con inteligencia artificial para Andalucía 2025”) y beca postdoctoral (Ref., DOC 01154, “selección de personal investigador doctor convocado mediante resolución de 21 de mayo de 2020”, PAIDI 2020). También ha sido parcialmente financiado por la Universidad a través del I Plan Propio de Investigación y Transferencia de la Universidad de Málaga. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Impacto de tráfico XR (eXtended Reality) en entornos Wi-Fi.

The objective of this study is to investigate the impact of eXtended Reality (XR) traffic on Wi-Fi networks and provide insights to improve the performance of XR applications. A network simulator was used to model Wi-Fi networks and generate traffic patterns for XR applications. The performance of Wi-Fi networks with videoconference traffic and XR traffic was compared in terms of throughput and delay.Este trabajo ha sido parcialmente financiado por el Centro para el Desarrollo Tecnológico Industrial (CDTI) del Ministerio de Ciencia e Innovación en el marco del proyecto MELODIC (Ref. IDI-20220551), el Ministerio de Asuntos Económicos y Transformación Digital y la Unión Europea - NextGenerationEU, en el marco del Plan de Recuperación, Transformación y Resiliencia y el Mecanismo de Recuperación y Resiliencia bajo el proyecto MAORI y la Junta de Andalucía a través de la Secretaría General de Universidades, Investigación y Tecnología con beca predoctoral (Ref. PREDOC 01712). También ha sido parcialmente financiado por la Universidad de Málaga a través del II Plan Propio de Investigación, Transferencia y Divulgación Científica, Campus de Excelencia Internacional Andalucía Tech

Measuring Key Quality Indicators in Cloud Gaming: Framework and Assessment Over Wireless Networks

Cloud Gaming is a cutting-edge paradigm in the video game provision where the graphics rendering and logic are computed in the cloud. This allows a user’s thin client systems with much more limited capabilities to offer a comparable experience with traditional local and online gaming but using reduced hardware requirements. In contrast, this approach stresses the communication networks between the client and the cloud. In this context, it is necessary to know how to configure the network in order to provide service with the best quality. To that end, the present work defines a novel framework for Cloud Gaming performance evaluation. This system is implemented in a real testbed and evaluates the Cloud Gaming approach for different transport networks (Ethernet, WiFi, and LTE (Long Term Evolution)) and scenarios, automating the acquisition of the gaming metrics. From this, the impact on the overall gaming experience is analyzed identifying the main parameters involved in its performance. Hence, the future lines for Cloud Gaming QoE-based (Quality of Experience) optimization are established, this way being of configuration, a trendy paradigm in the new-generation networks, such as 4G and 5G (Fourth and Fifth Generation of Mobile Networks)