Optimization for 6G Wireless Communications with Heterogeneous VR and Non-VR 360-Degree Videos: A Differentiated Reinforcement Learning Approach

Virtual Reality (VR) and its reliance on 360° videos are pivotal in delivering a seamless, immersive experience. In the era of emerging 6G technology, where diverse mobile devices are increasingly prevalent, optimizing Quality of Experience (QoE) becomes critical. This is especially true in applications that integrate both VR and non-VR modes. The focus on 6G highlights its capacity to cater to these varying requirements, ensuring high-quality video transmission across different platforms and user experiences. This paper introduces two novel algorithms: Separated Input Differentiated Output (SIDO) and Merged Input Differentiated Output (MIDO). These algorithms are designed to optimize resolution and power allocations in downlink wireless communication, catering to both non-VR and VR users within a chunk-based structure. By encapsulating diverse parameters like subjective perceptual video quality, chunk success rate, and cybersickness into our comprehensive QoE model, we present an approach to address challenges inherent to 360° video optimization. Our deep reinforcement learning algorithms, SIDO and MIDO, further refine the optimization process. Extensive experiments reveal the efficacy of our methodologies. This work, at its core, aims to bridge the gap between technical optimization and user experience, ensuring seamless integration of users.