Separating distributed source coding from network coding

Ramamoorthy, Aditya; Jain, Kamal; Chou, Philip A.; Effros, Michelle

doi:10.1109/tit.2006.874534

Cited by 78 publications

(94 citation statements)

References 20 publications

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“…correlated sources was first examined by Ho et al [2]. The work of Ramamoorthy et al [8] showed that in general separating distributed source coding and network coding is suboptimal except in the case of two sources and two terminals. A practical approach to transmitting correlated sources over a network was considered by Wu et al [10].…”

Section: Introductionmentioning

confidence: 99%

Communicating the sum of sources in a 3-sources/3-terminals network

Langberg

Ramamoorthy

2009

2009 IEEE International Symposium on Information Theory

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In this work we study the communication problem in general, and show that even for the case of three sources and three terminals, a single path connecting source/terminal pairs does not suffice to communicate P Xi. We then present an efficient encoding scheme which enables the communication of P Xi for the three sources, three terminals case, given that each source terminal pair is connected by two edge disjoint paths. Our encoding scheme includes a structural decomposition of the network at hand which may be found useful for other coding problems as well.

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Section: Introductionmentioning

confidence: 99%

Communicating the sum of sources in a 3-sources/3-terminals network

Langberg

Ramamoorthy

2009

2009 IEEE International Symposium on Information Theory

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“…(18) where is defined in (4). The formulation is the same as SUBSET-MIN-COST (5) except that we remove (8), and add (16) and (17), that are elemental inequalities, which guarantee that all Shannon type inequalities are satisfied [21]. The constraints in (16) and (17) (14)- (18)].…”

Section: A Analysis Of the Gap Between The Coded Case And The Subsetmentioning

confidence: 99%

Minimum Cost Mirror Sites Using Network Coding: Replication versus Coding at the Source Nodes

Huang

Ramamoorthy

Médard

2011

IEEE Trans. Inform. Theory

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Abstract-Content distribution over networks is often achieved by using mirror sites that hold copies of files or portions thereof to avoid congestion and delay issues arising from excessive demands to a single location. Accordingly, there are distributed storage solutions that divide the file into pieces and place copies of the pieces (replication) or coded versions of the pieces (coding) at multiple source nodes.We consider a network which uses network coding for multicasting the file. There is a set of source nodes that contains either subsets or coded versions of the pieces of the file. The cost of a given storage solution is defined as the sum of the storage cost and the cost of the flows required to support the multicast. Our interest is in finding the storage capacities and flows at minimum combined cost. We formulate the corresponding optimization problems by using the theory of information measures. In particular, we show that when there are two source nodes, there is no loss in considering subset sources. For three source nodes, we derive a tight upper bound on the cost gap between the coded and uncoded cases. We also present algorithms for determining the content of the source nodes.

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“…DSC (distributed source coding) is combined with network coding schemes [14][15][16][17] in the gathering of correlated data. Alternatively, network coding techniques are used while jointly performing data compression [18,19].…”

Section: Introductionmentioning

confidence: 99%

Approximate decoding approaches for network coded correlated data

Park¹,

Thomos²,

Frossard³

2013

Signal Processing

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a b s t r a c tThis paper considers a framework where data from correlated sources are transmitted with the help of network coding in ad hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth variations. We show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples showing how the proposed algorithm can be deployed in sensor networks and distributed imaging applications.

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Separating distributed source coding from network coding

Abstract: Abstract-This

Cited by 78 publications

References 20 publications

Communicating the sum of sources in a 3-sources/3-terminals network

Communicating the sum of sources in a 3-sources/3-terminals network

Minimum Cost Mirror Sites Using Network Coding: Replication versus Coding at the Source Nodes

Approximate decoding approaches for network coded correlated data

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