# A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2209.05761 · HTML: https://mlsanity.com/paper/2209.05761/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2022.

## Built on by (most cited first, 5 of 5)

- [Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving](https://mlsanity.com/paper/2308.05234/) (2023)
- [Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons](https://mlsanity.com/paper/2510.01439/) (2025)
- [Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensors](https://mlsanity.com/paper/2409.17341/) (2024)
- [Service Placement in Small Cell Networks Using Distributed Best Arm Identification in Linear Bandits](https://mlsanity.com/paper/2506.22480/) (2025)
- [Graph Neural Assisted Actor-Critic for Latency-Efficient Edge Vision System](https://mlsanity.com/paper/2608.16142/) (2026)

## Abstract (arXiv)

5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.