A Survey on Efficient Edge-Based Video Streaming: Integrating AI-Powered Upscaling, Adaptive Delivery, and Latency Reduction

• Author(s): Hardik Jain; Riya Sagar; Snigdha Vijay; Monisha H. M.
• Paper ID: 1712471
• Page: 557-563
• Published Date: 08-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 6 December-2025

Abstract

Video streaming has become one of the dominant drivers of internet traffic, demanding scalable, low-latency, and high-quality delivery solutions. Traditional cloud-based streaming suffers from high latency and bandwidth stress under dynamic network conditions. This survey explores edge-based video streaming architectures that integrate AI-driven upscaling, adaptive bitrate delivery, and latency mitigation mechanisms. We analyze recent advancements in edge caching, reinforcement learning–based adaptive streaming, and AI-powered super-resolution. Further, we propose an architectural model combining edge computing and AI for optimized video delivery. The survey highlights current achievements, limitations, and open challenges, offering guidance for researchers and engineers working on next-generation streaming systems.

Keywords

Edge Computing, Adaptive Streaming, Latency Reduction, AI Upscaling, Deep Reinforcement Learning.

Citations

IRE Journals:
Hardik Jain, Riya Sagar, Snigdha Vijay, Monisha H. M. "A Survey on Efficient Edge-Based Video Streaming: Integrating AI-Powered Upscaling, Adaptive Delivery, and Latency Reduction" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 557-563 https://doi.org/10.64388/IREV9I6-1712471

IEEE:
Hardik Jain, Riya Sagar, Snigdha Vijay, Monisha H. M. "A Survey on Efficient Edge-Based Video Streaming: Integrating AI-Powered Upscaling, Adaptive Delivery, and Latency Reduction" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712471