Research Repository

Content-Aware Delivery of Scalable Video in Network Coding Enabled Named Data Networks

Bourtoulatze, E and Thomos, N and Saltarin, J and Braun, T (2018) 'Content-Aware Delivery of Scalable Video in Network Coding Enabled Named Data Networks.' IEEE Transactions on Multimedia, 20 (6). pp. 1561-1575. ISSN 1520-9210

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We propose a novel network coding (NC) enabled named data networking (NDN) architecture for scalable video delivery. Our architecture utilizes network coding in order to address the problem that arises in the original NDN architecture, where optimal use of the bandwidth and caching resources necessitates the coordination of the Interest forwarding decisions. To optimize the performance of the proposed network coding based NDN architecture and render it appropriate for transmission of scalable video, we devise a novel rate allocation algorithm that decides on the optimal rates of Interests sent by clients and intermediate nodes. The flow of Data packets achieved by this algorithm maximizes the average quality of the video delivered to the client population. To support the handling of Interest and Data packets when intermediate nodes perform network coding, we introduce the use of Bloom filters, which store efficiently additional information about the Interest and Data packets, and modify accordingly the standard NDN architecture. We also devise an optimized Interest forwarding strategy that implements the target rate allocation. The proposed architecture is evaluated for transmission of scalable video over PlanetLab topologies. The evaluation shows that the proposed scheme exploits optimally the available network resources.

Item Type: Article
Uncontrolled Keywords: Network coding; named data networking; scalable video; rate allocation; forwarding strategy
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 23 Nov 2017 12:21
Last Modified: 15 Jan 2022 00:38

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