Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/814
Title: Efficient Multimedia Transmission over Scalable IoT Architecture
Authors: Memos, Vasileios
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Cloud Computing
HEVC
IoT
Layered Architecture
Mathematical Model
Multimedia Streaming
Quality Scalability
Issue Date: 8-Jun-2018
Publisher: MECS Press
Source: International Journal of Computer Network and Information Security
Volume: 10
Issue: 6
First Page: 27
Last Page: 39
Abstract: Today the new technology concept, called “Internet of Things” (IoT), presents a significant field of research, due to the fact that it promises many advantages to the users worldwide. Besides this, multimedia file streaming tends to be a daily user habit, as a result of the evolution of the internet and the capabilities of devices, such as mobile phones and computers. This tendency has established Cloud Computing (CC) as a vital technology due to the fact that it reduces economic costs, improves accessibility and flexibility, while it increases the total network performance too. This paper demonstrates a new technological approach for IoT architecture, where a layered implementation with the use of many cloud servers can offer optimized streaming and download, because of the significant reduction of the transfer time costs and the storage space of the Main Cloud Server (MCS). Moreover, users will have the ability to choose or adapt to their devices’ restrictions, between low -up to -Ultra-High Definition (UHD) quality for multimedia access and downloading, thanks to anew quality scalability feature which is introduced and is based on the new High-Efficiency Video Coding (HEVC) compression standard. Experimental results show the efficiency of the proposed approach with math formulas and graphs.
URI: https://doi.org/10.5815/ijcnis.2018.06.03
https://ruomo.lib.uom.gr/handle/7000/814
ISSN: 2074-9090
2074-9104
Electronic ISSN: 2074-9104
Other Identifiers: 10.5815/ijcnis.2018.06.03
Appears in Collections:Department of Applied Informatics

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