Reduction of Padding Overhead for RLNC Media Distribution With Variable Size Packets

Taghouti, Maroua; Lucani, Daniel E.; Cabrera, Juan A.; Reisslein, Martin; Pedersen, Morten Videbæk; Fitzek, Frank H. P.

doi:10.1109/tbc.2019.2892594

Cited by 11 publications

(3 citation statements)

References 75 publications

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“…With increasing D , the double exponentially increasing packet length M may cause the practical issue of an excessive padding overhead. Such an issue can be effectively solved based on the methods proposed in [ 16 , 17 ].…”

Section: Framework Descriptionmentioning

confidence: 99%

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

Tang

Zheng

et al. 2022

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In complex network environments, there always exist heterogeneous devices with different computational powers. In this work, we propose a novel scalable random linear network coding (RLNC) framework based on embedded fields, so as to endow heterogeneous receivers with different decoding capabilities. In this framework, the source linearly combines the original packets over embedded fields based on a precoding matrix and then encodes the precoded packets over GF(2) before transmission to the network. After justifying the arithmetic compatibility over different finite fields in the encoding process, we derive a sufficient and necessary condition for decodability over different fields. Moreover, we theoretically study the construction of an optimal precoding matrix in terms of decodability. The numerical analysis in classical wireless broadcast networks illustrates that the proposed scalable RLNC not only guarantees a better decoding compatibility over different fields compared with classical RLNC over a single field, but also outperforms Fulcrum RLNC in terms of a better decoding performance over GF(2). Moreover, we take the sparsity of the received binary coding vector into consideration, and demonstrate that for a large enough batch size, this sparsity does not affect the completion delay performance much in a wireless broadcast network.

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Section: Framework Descriptionmentioning

confidence: 99%

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

Tang

Zheng

et al. 2022

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“…Moreover, since RaptorQ coding is a linear combination of intermediate symbols [60], one could specify a linear combination vector as part of the packet header. Such a linear combination vector could be represented with a packet header structure similar to the RLNC packet header structure [65], [66] and thus could keep track of linear recodings in intermediate network nodes. We employ the CodornicesRq (Release 2.1) implementation [67] of Rap-torQ for our evaluations.…”

Section: B Related Workmentioning

confidence: 99%

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

et al. 2020

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Fulcrum coding combines a high-field outer Random Linear Network Coding (RLNC) that generates outer coding expansion packets with a small-field inner RLNC that combines the source packets and the outer coding expansion packets. This two-layer Fulcrum coding allows flexible decoding in receivers with heterogeneous computational capabilities. Fulcrum coding has so far only been studied for conventional dense RLNC, which randomly selects all coding coefficients, and only for a statically fixed number of outer expansion packets. However, the probability that the coding coefficient row of a newly received packet is linearly independent of prior received coding coefficient rows (a prerequisite for successful decoding) is highly dynamic. We propose to exploit the dynamics of this probability to reduce the computational complexity of Fulcrum coding. In particular, we vary the density of non-zero coding coefficients, i.e., equivalently, the sparsity of coding coefficients, and the number of outer expansion packets to keep the complexity low while maintaining a reasonably high decoding probability. We introduce the general principles of dynamic sparsity and expansion packets (DSEP) for Fulcrum coding as well as two specific example DSEP policies. Our evaluations indicate that DSEP Fulcrum can increase the encoding throughput tenfold and increase the decoding throughput 1.4 to 4.3 fold while achieving decoding probabilities that are typically less than 1% lower than the conventional Fulcrum decoding probabilities. We also find that DSEP achieves somewhat higher encoding and decoding throughputs than the CodornicesRq (Release 2.1) implementation of RaptorQ block coding for small blocks (generations) of source packets, while RaptorQ is substantially faster for large generation sizes. Furthermore, we develop and evaluate an elementary DSEP recoding mechanism that achieves a recoding throughput more than double the decoding throughput.

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“…Data size m (symbol size) was varied between m = 1024 representing short data packets, m = 1536 which is close to the typical Ethernet Maximum Transfer Unit (MTU) of 1500 bytes but a multiple of 512, as well as m = 4096 and m = 16384 Bytes which may be used for data center storage. (Unequal packet sizes can be accommodated with padding or other techniques [62]. )…”

Section: ) Test Matrices and Parameter Settingsmentioning

confidence: 99%

Progressive Multicore RLNC Decoding With Online DAG Scheduling

2019

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A complete generation of packets coded with Random Linear Network Coding (RLNC) can be quickly decoded on a multicore system by scheduling the involved matrix block operations in parallel with an offline (pre-recorded) directed acyclic graph (DAG). The waiting for a complete generation of packets can be avoided with progressive RLNC decoding that commences the decoding (and can decode some packets) before all packets in a generation have been received. This article develops and evaluates a novel progressive RLNC decoding strategy based on the principle of DAG scheduling of parallel matrix block operations. The novel strategy involves helper matrices for conducting the Gauss Jordan elimination based on rows of blocks of matrix elements. The matrix block computations are dynamically scheduled by an online DAG which permits branching, e.g., to skip unnecessary matrix block operations. The throughput and delay of the novel progressive RLNC decoding strategy are evaluated with experiments on two heterogeneous multicore processor boards. The novel progressive RLNC decoding achieves throughput levels on par with stateof-the-art non-progressive (full-generation) RLNC decoding and achieves three times higher throughput than the fastest (highest-throughput) known progressive RLNC decoder for small generation sizes and short data packets. Also, our progressive RLNC decoding greatly reduces receiver delays for moderate to large generation sizes; the delay reductions are particularly pronounced when a low-delay RLNC version is employed (e.g., reduction to one tenth of the non-progressive decoding delay for a generation size of 256 packets). INDEX TERMS Directed acyclic graph (DAG), helper matrix, heterogeneous multicore architecture, online scheduling, parallel computing, random linear network coding (RLNC).

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Reduction of Padding Overhead for RLNC Media Distribution With Variable Size Packets

Cited by 11 publications

References 75 publications

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

Scalable Network Coding for Heterogeneous Devices over Embedded Fields

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

Progressive Multicore RLNC Decoding With Online DAG Scheduling

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