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

References

[1] Li, Z., Mao, H., Zhu, J., Jiang, Z., Ghodsi, A., Rexford, J., & Stoica, I. (2020). Deep reinforcement learning for internet congestion control

[2] Xu, M., Yu, H., Pan, J., & Sun, L. (2018). HTTP/2-based low-latency live video streaming.

[3] B. Mengistu, “Deep-Learning Realtime Upsampling Techniques in Video Games,” University of Minnesota Morris Digital Well, 2023.

[4] Lundkvist, F. (2021). Deep upscaling for video streaming: a case evaluation at SVT. KTH Royal Institute of Technology, Stockholm, Sweden.

[5] M. Hu, Z. Luo, A. Pasdar, Y. C. Lee, Y. Zhou, and D. Wu, “Edge-Based Video Analytics: A Survey,” 2023.

[6] K. Nassisid, T. David, and K. Muhammad, “Adaptive Video Streaming over 6G Networks: Buffer Control and User Behavior Analysis,” arXiv preprint, 2024.

[7] M. Choi, A. No, M. Ji, and J. Kim, “Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks,” IEEE Trans. Wireless Commun., vol. 18, pp. 5705–5718, 2019.

[8] T. Chen et al., “SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video Streaming,” in Proc. ACM SIGCOMM, 2024.

[9] I. Yeregui et al., “Leveraging 5G Physical Layer Monitoring for Adaptive Remote Rendering in XR Applications,” 2025.

[10] Dharuman et al., “Edge-Optimized HLS/DASH and ML-Based CDN Optimization,” 2023.

[11] STL Partners, “3 Reasons Why Edge Will Change Video Streaming,” STL Partners, 2025.

[12] Muvi, “The Role of Edge Computing in Video Streaming,” Muvi, 2025.

[13] M. Bilal and M. Erbad, “Edge-Based Transcoding and Containerization for Video Streaming,” 2017.

[14] J.-H. Choi et al., “Dynamic Video Delivery Using Deep Reinforcement Learning,” 2025.

[15] A. Ghosh, S. Iyengar, S. Lee, A. Rathore, and V. N. Padmanabhan, “REACT: Streaming Video Analytics on the Edge,” in Proc. 8th ACM/IEEE Conf. on Internet of Things Design and Implementation (IoTDI), 2023.

[16] National Science Foundation, “Adaptive Bitrate Control with Edge-Aware AI,” NSF, 2025.

[17] M. Bilal et al., “FoV-Aware Edge Caching for 360° Video,” 2018.

[18] ElasticEdge, “Kubernetes-Based Elastic Edge Frameworks for Live Analytics,” 2022. 

@article{1712471,
      author = {Hardik Jain, Riya Sagar, Snigdha Vijay, Monisha H. M.},
      title = {A Survey on Efficient Edge-Based Video Streaming: Integrating AI-Powered Upscaling, Adaptive Delivery, and Latency Reduction},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {6},
      pages = {557-563},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712471.pdf},
      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.},
      month = {December},
      doi = {https://doi.org/10.64388/IREV9I6-1712471}
  }