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.
@article{arxiv.2105.02409,
title = {Multimedia Edge Computing},
author = {Zhi Wang and Wenwu Zhu and Lifeng Sun and Han Hu and Ge Ma and Ming Ma and Haitian Pang and Jiahui Ye and Hongshan Li},
journal= {arXiv preprint arXiv:2105.02409},
year = {2021}
}
Comments
20 pages, 9 figures. arXiv admin note: text overlap with arXiv:1702.07627