Multi-view video streaming requires significant bandwidth to deliver all viewpoints at high quality. Prioritizing high-quality video for the main view only may help resolve bandwidth management issues. However, significant viewpoint delay can occur during view switching. This research is divided into three parts. In the first part, we discuss the viewpoint delay problem, introduce multi-view video quality of experience (MV-QoE) as a metric to measure user satisfaction and propose a multi-view video priority (MVP) algorithm for adaptive streaming. In the second part, we analyze factors affecting viewpoint delay and propose a segment redownloading method to refetch low-quality video segments. While previous methods adopt heuristic approaches, in the last part, we propose a reinforcement learning model to improve the performance of MV-QoE. The goal of this research is to provide multi-view video streaming services with better quality, smoothness, continuity, and seamless viewpoint transitions.