QoE Optimization for Virtual Reality Services in Multi-RIS-Assisted Terahertz Wireless Networks

The immersive experience and 360-degree visual stimulation offered by virtual reality (VR) have contributed to its widespread adoption in games, education, and healthcare. The quality of experience (QoE), as a significant performance indicator, is used to measure user experience from subjective and objective perspectives and is required to satisfy high data rate, low delay, and high reliability in the wireless VR system. To achieve a higher data rate for VR users, a terahertz (THz) network is deployed. However, THz frequency experiences severe signal attenuation due to complex indoor obstacles, which can be alleviated by utilizing a reconfigurable intelligent surface (RIS) equipped with programmable metamaterial reflective elements. Taking inspiration from these considerations, this paper investigates a new framework for indoor multi-user multi-RIS-assisted THz wireless VR systems. Based on the scenario, an optimization problem is formulated to maximize the QoE by jointly optimizing the passive beamforming at RIS, the transmit power allocation among VR users, and the rendering capacity allocation among virtual objects. To achieve an optimal solution, we decompose the optimization problem into two stages: stage-1 aims to minimize BER and maximize data transmission rate, while stage-2 aims to maximize rendering capacity among virtual objects. Objective function conversion and alternative optimization (AO) methods are employed to address the two problems. Extensive simulations are conducted to validate the feasibility of the proposed system model and to showcase the superior performance of the proposed method in terms of QoE compared to other baseline methods.