Dataset Information

An enhanced heuristic XoR network coding-based method for high quality video streaming over VANETs.

ABSTRACT: One of the most important challenges in live video streaming in mobile vehicular networks is the optimal use of broadband and point-to-point packet delay. Recent studies show that the sheer use of frames flow compression methods (such as H.264 or HEVC) and the proper communication overlay, such as Peer-to-Peer (P2P), has no absolute influence on increasing the quality of received video in VANET networks. Therefore, the use of an appropriate data exchange method, such as network coding, seems to be of great importance. Compared to Random Network Coding (RNC), XoR Network Coding (XNC) method has the least computational load for the network which is an important factor in optimal use of limited energy of nodes in a wireless network. The basic problem in XNC is that when a node is supposed to combine several frames and transmit them through an encoded frame, how this combination could be made to enable other nodes of the network to be broadcasted through receiving this packet and how can the available packets in their buffers decode as well as extract the largest number of frames in order to experience a higher video quality. To fulfil this aim, an encoding intelligent method is required which is based on the buffers' status of neighbours. In the proposed method in this article, the best frame combination is reached through buffers status of neighbours and AHP methods or AHP-TOPSIS methods, and the encoded frames are broadcasted through XNC. Simulation results show that due to the reduction in number of transmitted packets in the network, parameters such as congestion and point-to-point delay are significantly reduced and vehicles experience a higher video quality compared with other similar methods.

SUBMITTER: Mosaarab M

PROVIDER: S-EPMC6598775 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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An enhanced heuristic XoR network coding-based method for high quality video streaming over VANETs.

Mosaarab Maryam M Barekatain Behrang B Raahemifar Kaamran K Movahednejad Homa H

PloS one 20190628 6

One of the most important challenges in live video streaming in mobile vehicular networks is the optimal use of broadband and point-to-point packet delay. Recent studies show that the sheer use of frames flow compression methods (such as H.264 or HEVC) and the proper communication overlay, such as Peer-to-Peer (P2P), has no absolute influence on increasing the quality of received video in VANET networks. Therefore, the use of an appropriate data exchange method, such as network coding, seems to ...[more]

PMID: 31251749

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Project description:The advances in high throughput omics technologies have made it possible to characterize molecular interactions within and across various species. Alignments and comparison of molecular networks across species will help detect orthologs and conserved functional modules and provide insights on the evolutionary relationships of the compared species. However, such analyses are not trivial due to the complexity of network and high computational cost. Here we develop a mixture of global and local algorithm, BinAligner, for network alignments. Based on the hypotheses that the similarity between two vertices across networks would be context dependent and that the information from the edges and the structures of subnetworks can be more informative than vertices alone, two scoring schema, 1-neighborhood subnetwork and graphlet, were introduced to derive the scoring matrices between networks, besides the commonly used scoring scheme from vertices. Then the alignment problem is formulated as an assignment problem, which is solved by the combinatorial optimization algorithm, such as the Hungarian method. The proposed algorithm was applied and validated in aligning the protein-protein interaction network of Kaposi's sarcoma associated herpesvirus (KSHV) and that of varicella zoster virus (VZV). Interestingly, we identified several putative functional orthologous proteins with similar functions but very low sequence similarity between the two viruses. For example, KSHV open reading frame 56 (ORF56) and VZV ORF55 are helicase-primase subunits with sequence identity 14.6%, and KSHV ORF75 and VZV ORF44 are tegument proteins with sequence identity 15.3%. These functional pairs can not be identified if one restricts the alignment into orthologous protein pairs. In addition, BinAligner identified a conserved pathway between two viruses, which consists of 7 orthologous protein pairs and these proteins are connected by conserved links. This pathway might be crucial for virus packing and infection.

Dataset Information

An enhanced heuristic XoR network coding-based method for high quality video streaming over VANETs.

Publications

An enhanced heuristic XoR network coding-based method for high quality video streaming over VANETs.

Similar Datasets

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