Enhancing Immersive Streaming CDN through MEC-EdgeAI in NG-EPON & 6G-Xhaul Access Network

The integration of Multi-access Edge Computing (MEC) and Edge Artificial Intelligence (EdgeAI) in Next-Generation Passive Optical Networks (NG-EPON) and 6G-Xhaul access networks presents an innovative method to improve immersive live video streaming. EdgeAI offers a potential solution for addressing the quality of service (QoS), dynamic bandwidth management, and reduced latency needs of these contents. The primary motivation behind this research is to enhance the efficiency and performance of Content Delivery Networks (CDNs) when delivering immersive live content. The proposed architecture utilizes Long Short-Term Memory (LSTM) networks to predict bandwidth requirements and dynamically adjust link rates. This ensures the efficient and reliable delivery of immersive live video content with reduced latency. The simulations conducted to evaluate system performance demonstrate substantial improvements in mean packet delay, packet drop probability, and system throughput, thus confirming the effectiveness of the proposed approach.

Authors

Institutions

Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/ictc62082.2024.10827178
Citations
1
Primary Topic
Advanced Photonic Communication Systems
Type
article
Field-Weighted Citation Impact
0.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Immersive Streaming CDN through MEC-EdgeAI in NG-EPON & 6G-Xhaul Access Network

Razat Kharga, I‐Shyan Hwang, Andrew Tanny Liem, AliAkbar Nikoukar
1 citations
Advanced Photonic Communication Systems
0.21
article

Enhancing Immersive Streaming CDN through MEC-EdgeAI in NG-EPON & 6G-Xhaul Access Network

Razat Kharga, I‐Shyan Hwang, Andrew Tanny Liem, AliAkbar Nikoukar
article en
1 citations

Abstract

The integration of Multi-access Edge Computing (MEC) and Edge Artificial Intelligence (EdgeAI) in Next-Generation Passive Optical Networks (NG-EPON) and 6G-Xhaul access networks presents an innovative method to improve immersive live video streaming. EdgeAI offers a potential solution for addressing the quality of service (QoS), dynamic bandwidth management, and reduced latency needs of these contents. The primary motivation behind this research is to enhance the efficiency and performance of Content Delivery Networks (CDNs) when delivering immersive live content. The proposed architecture utilizes Long Short-Term Memory (LSTM) networks to predict bandwidth requirements and dynamically adjust link rates. This ensures the efficient and reliable delivery of immersive live video content with reduced latency. The simulations conducted to evaluate system performance demonstrate substantial improvements in mean packet delay, packet drop probability, and system throughput, thus confirming the effectiveness of the proposed approach.

Yasouj University (IR), Klabat University (ID), Yuan Ze University (TW)
Affordable and clean energy
Openalex Percentile: Top 44%
Advanced Photonic Communication Systems
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Enhancing Immersive Streaming CDN through MEC-EdgeAI in NG-EPON & 6G-Xhaul Access Network — Razat Kharga, I‐Shyan Hwang, et al. · (2024) | TGRS Research Map | TGRS