Adaptive VR Video Data Transmission Method Using Mobile Edge Computing Based on AIoT Cloud VR

Abstract

Aiming at the high requirements of cloud service-based virtual reality in AIoT for data transmission rate and delay sensitivity, a cloud VR system scheme based on MEC (Mobile Edge Computing) is proposed, which mainly incorporates viewpoint-based VR video data processing and hybrid digital-to-analog (HDA) transmission optimization and can be served for AIoT transmission filed. Firstly, a learning-driven multiaccess MEC offloading strategy is designed, in which the VR terminal automatically selects the optimal MEC server for task offloading, thereby effectively improving network efficiency and reducing service delay. Secondly, the progressive transmission of the VR data is realized through viewpoint-aware dynamic streaming based on RoI (region of interest) and the priorities of different objects. The transmission priority of each object in the scene is determined through the ROI layering, which effectively solves the contradiction between the large data volume in the VR scenes and the network bandwidth limitation when applied in AIoT domain, and further improves the real-time performance of the system. Then, the HDA (hybrid digital-analog) technique is introduced to optimize the transmission. Finally, the base station protocol stack is modified on the basis of the LTE (Long-Term Evolution) system, and the MEC technology is integrated to realize a complete cloud VR system in AIoT. The experimental results show that compared with other advanced schemes, the proposed scheme can achieve more robust and efficient data transmission performance and provide better VR user experience.