Multimedia Edge Computing

TL;DR

This paper reviews recent advances in multimedia edge computing, emphasizing distributed machine learning, edge-cloud collaboration, and strategies to enhance quality of experience for multimedia services.

Contribution

It offers a comprehensive overview of recent research, industrial solutions, and future directions in multimedia edge computing, highlighting fundamental design guidelines.

Findings

Migration of cloud multimedia to edge-aware systems

Use of reinforcement and online learning at edge devices

Guidelines for designing effective multimedia edge strategies

Abstract

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.