TY - GEN
T1 - Machine Learning-based Edge Caching for Video Streaming in 5G Networks
AU - Benmimoune, Abderrahmane
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The advent of 5G networks has brought significant advancements in the Quality of Service (QoS) provided to various applications, including video streaming. However, the increasing demand for high-quality video streaming, coupled with the need for low latency and improved user experience, poses challenges for the existing network architecture. Recently, there have been several proposals to utilize machine learning techniques in order to improve the QoS for mobile video users. These techniques aim to enhance various aspects of video delivery, such as video streaming, video compression, and video adaptation. This paper aims to explore the use of edge caching machine learning-based technique for video streaming services. In this paper, proof-of-concept experiments and the setup of a Hybrid Cloud-Edge Architecture with Amazon Web Services are presented. The experimental results demonstrate that applying machine learning to cloud-edge caching architecture is both feasible and effective.
AB - The advent of 5G networks has brought significant advancements in the Quality of Service (QoS) provided to various applications, including video streaming. However, the increasing demand for high-quality video streaming, coupled with the need for low latency and improved user experience, poses challenges for the existing network architecture. Recently, there have been several proposals to utilize machine learning techniques in order to improve the QoS for mobile video users. These techniques aim to enhance various aspects of video delivery, such as video streaming, video compression, and video adaptation. This paper aims to explore the use of edge caching machine learning-based technique for video streaming services. In this paper, proof-of-concept experiments and the setup of a Hybrid Cloud-Edge Architecture with Amazon Web Services are presented. The experimental results demonstrate that applying machine learning to cloud-edge caching architecture is both feasible and effective.
KW - 5G Network
KW - Edge Caching
KW - Machine learning
KW - Video Streaming
UR - https://www.scopus.com/pages/publications/85189929779
U2 - 10.1109/ICRAIE59459.2023.10468338
DO - 10.1109/ICRAIE59459.2023.10468338
M3 - Contribution to conference proceedings
AN - SCOPUS:85189929779
T3 - 8th International Conference on Recent Advances and Innovations in Engineering: Empowering Computing, Analytics, and Engineering Through Digital Innovation, ICRAIE 2023
BT - 8th International Conference on Recent Advances and Innovations in Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023
Y2 - 2 December 2023 through 3 December 2023
ER -