About this opportunity:
In the Ericsson research area Artificial Intelligence (AI) we study and develop Machine Learning and AI technologies for intelligent systems. Our research spans technologies for intelligent automation, novel applications of ML/AI to differentiate Ericsson’s portfolio and the study and development of frameworks for AI in products and services. The Architectures and Frameworks group in Luleå, Sweden, work with next generation AI platforms, tools and frameworks and research on AI to further improve energy efficiency in our products. As real-time video streaming, such as cloud gaming, video conferencing etc., become increasingly popular, ensuring high video quality is crucial for providing an optimal user experience. Video quality assessment with interactive services involves evaluating various factors such as resolution, frame rate, latency, and compression artefacts, which can significantly impact user satisfaction. Traditional video quality metrics and user experience assessments may not fully capture the unique challenges of the environments of these services, where network variability play a critical role. Advanced methods for real-time quality assessment and adaptation are necessary to address these challenges. This thesis aims to explore and develop innovative approaches for evaluating and measure video quality in highly interactive services.

What you will do:
This project contains multiple studies that can be performed by two students as a group or as separate activities. These smaller studies are part of an ongoing Quality of Experience investigation regarding highly interactive remote rendered services such as cloud gaming, remote rendered VR/XR gaming, teleconferencing (Teams/Facetime/Zoom…) and will contribute with important building blocks for advancing understanding in this field.
Firstly, performing subjective testing with an interactive service such as cloud gaming is a difficult task. LTU has designed a base for a test envir