In this paper, we investigate the recent studies on multimedia edge computing, from sensing not only traditional visual/audio data but also individuals' geographical preference and mobility behaviors, to performing distributed machine learning over such data using the joint edge and cloud infrastructure and using evolutional strategies like reinforcement learning and online learning at edge devices to optimize the quality of experience for multimedia services at the last mile proactively. We provide both a retrospective view of recent rapid migration (resp. merge) of cloud multimedia to (resp. and) edge-aware multimedia and insights on the fundamental guidelines for designing multimedia edge computing strategies that target satisfying the changing demand of quality of experience. By showing the recent research studies and industrial solutions, we also provide future directions towards high-quality multimedia services over edge computing.

翻译：在本文中,我们调查了最近关于多媒体边缘计算的研究,这些研究不仅包括传统的视觉/视听数据,还包括个人的地理偏好和移动行为,还包括利用联合边缘和云层基础设施,对这些数据进行分布式机器学习,以及利用边缘设备强化学习和在线学习等进化战略,积极主动地在最后一英里优化多媒体服务的经验质量。我们提供了对最近云层多媒体迅速迁移(合并)到(重复)边缘认识多媒体的回溯性观察,以及对设计多媒体边缘计算战略的基本准则的深入了解,这些战略旨在满足不断变化的经验质量需求。我们通过展示最近的研究和工业解决方案,还提供了未来方向,向高质量多媒体服务超越边缘计算的方向。