What is it about?
We design a digital twin-empowered edge computing (DTEC) system and formulate a maximization problem that considers attention-based resolution perception to maximize the quality of experience (QoE). This problem optimizes the allocation of computing and bandwidth resources while adapting the attention-based resolution of the VR content. We then apply continual reinforcement learning (CRL) to enable adaptive attention-based resolution VR streaming in a time-varying environment.
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Why is it important?
Our framework reduces the average latency, QoE, and successful delivery rate and meets the horizon fairness QoE requirements and performance over long-term execution while ensuring system scalability with the increasing number of users.
Perspectives
Scalability is essential for real-time VR content streaming to enhance the users' experience.
Ahmad ALHILAL
Hong Kong University of Science and Technology
Read the Original
This page is a summary of: QoE Optimization for VR Streaming: a Continual RL Framework in Digital Twin-empowered MEC, December 2023, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/globecom54140.2023.10437100.
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