多媒体边缘计算
Multimedia Edge Computing
摘要：
在本文中，我们调查了多媒体边缘计算的最新研究，从感知不仅是传统的视觉/音频数据，但个人的地理偏好和移动行为，涉及使用联合边缘和云基础设施并在边缘设备处使用诸如强化学习和在线学习的进化策略来对这样的数据执行分布式机器学习，以优化边缘设备处的多媒体服务的体验质量。最后一公里主动出击我们提供了一个回顾性的观点，最近的快速移民（分别。合并）的云多媒体到（分别以及）边缘感知多媒体和关于设计多媒体边缘计算策略的基本准则的见解，这些策略旨在满足不断变化的体验质量需求。通过展示最近的研究和工业解决方案，我们还提供了通过边缘计算实现高质量多媒体服务的未来方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
