Toward High-Quality Low-Latency 360° Video Streaming With Edge–Client Collaborative Caching and Super-Resolution

360°video streaming, as an immersive and interactive form of video presentation, has consistently strived for higher video quality and lower latency. A lot of effort has been devoted to improving video quality and reducing latency by utilizing the storage or computing resources of the edge/end layer with edge caching and super-resolution (SR) techniques, respectively. However, it has been conspicuously ignored by existing work that fully leveraging the aggregated caching and computing resources of both edge and end layers can further improve video quality and reduce latency. To this end, in this article, we propose a multiagent deep reinforcement learning-based edge–client collaborative caching and SR (ECCSR) strategy for high-quality low-latency 360° video streaming. First, we construct a Quality of Experience (QoE) function that involves not only video quality, temporal smoothness, and rebuffering time, but also device energy consumption. Subsequently, we formulate the problem of ECCSR as a multiagent cooperative Markov decision process with the goal of maximizing users’ average QoE. Furthermore, to cope with the decision coupling between agents, an adaptive learning-based multiagent double actors regularized critics (AL-MADARC) algorithm is developed to help agents make optimal collaborative caching and SR decisions. Through extensive experiments using real-world data sets, we show that ECCSR makes a great improvement in users’ average QoE compared to existing strategies.