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.

中文翻译：

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多媒体边缘计算

在本文中，我们调查了多媒体边缘计算的最新研究，从感知传统的视觉/音频数据到个人的地理偏好和移动行为，到使用联合边缘和云基础架构对此类数据进行分布式机器学习以及使用诸如在边缘设备上进行强化学习和在线学习等进化策略，以主动优化最后一英里的多媒体服务的体验质量。我们提供了有关云多媒体最近向边缘和边缘边缘多媒体快速迁移（重新合并）的回顾性观点，以及对旨在满足不断变化的体验质量需求的设计多媒体边缘计算策略的基本准则的见解。 。通过展示最近的研究和工业解决方案，