Video streaming on fog and edge computing layers: A systematic mapping study

Abstract

Video streaming has become increasingly dominant in internet traffic and daily applications, significantly influenced by emerging technologies such as autonomous cars, augmented reality, and immersive videos. The computing community has extensively discussed aspects like latency, device power consumption, 5G, and computing. The advent of 6G technology, an emerging communication paradigm beyond existing technologies, promises to revolutionize these areas with enhanced bandwidth, reduced latency, and advanced connectivity features. Fog and Edge Computing environments intensify data generation, control, and analysis at the network edge. Consequently, adopting metrics such as QoE (Quality of Experience) and QoS (Quality of Service) is crucial for developing adaptive streaming services that dynamically adjust video quality based on network conditions. This work systematically maps the literature on video streaming approaches in Fog and Edge Computing that utilize QoS and QoE metrics to evaluate performance in managing Live Streaming and Streaming on Demand. The results highlight the most used metrics and discuss resource management strategies, providing valuable insights for developing new approaches and enhancing existing communication protocols like DASH (Dynamic Adaptive Streaming over HTTP) and HLS (HTTP Live Streaming).