Fairness-Aware Multicategory 360$^\circ$ Video Streaming in Cloud-Edge Collaboration Networks

360$^\circ$video streaming emerges as an innovative video presentation form that offers users an immersive and interactive experience, where the quality of experience (QoE) is a vital indicator to measure user viewing perception. In multicategory 360$^\circ$video streaming, existing QoE-driven approaches typically assume a fixed request distribution to enhance users' average QoE, prioritizing the optimization of edge caching and bitrate selection decisions for the video category with a larger request number. Inevitably, these unfair approaches would lead to average QoE reduction in real-world scenarios, in which the request distribution exhibits significant variations and is challenging to predict accurately. To this end, we propose a fairness-aware 360$^\circ$video streaming strategy in cloud-edge collaboration networks for improving users' average QoE. Specifically, we first formulate the joint edge caching and bitrate selection problem as a multi-agent cooperative input-driven Markov decision process to maximize users' average QoE and guarantee QoE fairness for users. Subsequently, we devise an adaptive learning-based multi-agent deep reinforcement learning (MADRL) approach, which can adaptively adjust the learning rate of each agent according to the dynamic user request distribution, thus helping agents make optimal decisions. Finally, experimental results on real-world datasets show that the proposed algorithm significantly improves users' average QoE while ensuring QoE fairness for users.