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Abstract
The COVID-19 pandemic has affected the world socially and economically changing behaviors towards medical facilities, public gatherings, workplaces, and education. Educational institutes have been shutdown sporadically across the globe forcing teachers and students to adopt distance learning techniques. Due to the closure of educational institutes, work and learn from home methods have burdened the network resources and considerably decreased a viewer’s Quality of Experience (QoE). The situation calls for innovative techniques to handle the surging load of video traffic on cellular networks. In the scenario of distance learning, there is ample opportunity to realize multi-cast delivery instead of a conventional unicast. However, the existing 5G architecture does not support service-less multi-cast. In this article, we advance the case of Virtual Network Function (VNF) based service-less architecture for video multicast. Multicasting a video session for distance learning significantly lowers the burden on core and Radio Access Networks (RAN) as demonstrated by evaluation over a real-world dataset. We debate the role of Edge Intelligence (EI) for enabling multicast and edge caching for distance learning to complement the performance of the proposed VNF architecture. EI offers the determination of users that are part of a multicast session based on location, session, and cell information. Moreover, user preferences and network’s contextual information can differentiate between live and cached access patterns optimizing edge caching decisions. While exploring the opportunities of EI-enabled distance learning, we demonstrate a significant reduction in network operator resource utilization and an increase in user QoE for VNF based multicast transmission.
Original language | English |
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Article number | 1092 |
Number of pages | 14 |
Journal | Sensors |
Volume | 22 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Feb 2022 |
Keywords
- Distance learning
- Edge caching
- Edge intelligence
- Video multicast
- eMBMS
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Dive into the research topics of 'Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution'. Together they form a unique fingerprint.Projects
- 1 Finished
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EX-QNRF-NPRPS-38: AI-Based Next Generation Edge Platform for Heterogeneous Services using 5G Technologies
Abdallah, M. M. (Principal Investigator), Abegaz, M. S. (Post Doctoral Fellow), Hevesli, M. (Graduate Student), Student-1, G. (Graduate Student), Saad, M. R. (Consultant), Assistant-1, R. (Research Assistant), Assistant-3, R. (Research Assistant), Mohamed, D. A. (Principal Investigator), Al-Jaber, D. H. (Principal Investigator), Chiasserini, P. C. F. (Principal Investigator) & Al Fuqaha, A. (Lead Principal Investigator)
11/04/21 → 30/09/24
Project: Applied Research