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Title	Video Data Offloading Techniques In Mobile Edge Computing: A Survey
ID_Doc	61096
Authors	Ma H.; Ji B.; Wu H.; Xing L.
Year	2024
Published	Physical Communication, 62
DOI	http://dx.doi.org/10.1016/j.phycom.2023.102261
Abstract	Driven by the Quality of Experience (QoE) demands for video analysis applications within contexts such as smart cities, Industrial Internet of Things (IoT), and Internet of Vehicles (IoV), networks are confronted with numerous challenges, including, but not limited to, the requirements for ultra-high bandwidth, ultra-low latency, reliability, and stability. Mobile Edge Computing (MEC) as a technology aims to decentralize computational and storage resources closer to data sources and end-users, meeting the demands of computation-intensive and latency-sensitive video analysis applications and thereby effectively addressing QoE requirements. This has led to a new intersection, edge video offloading, which has attracted widespread attention. However, to date, there are but a few extant papers on this topic with limited relevance. To bridge this gap, we offer a comprehensive review of the state-of-the-art approaches in computational offloading techniques applied to video data, innovatively revisiting existing task offloading technologies from the perspective of mobility of edge server nodes, including solutions based on both fixed and dynamic MEC servers. Additionally, we explore the differences in video offloading techniques between scenarios involving a single MEC server and those involving multiple MEC servers. Meanwhile, open challenges are presented to direct future research in this field. © 2023 Elsevier B.V.
Author Keywords	Computation offloading; Mobile edge computing; Real-time video analysis

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