Progressive Multicore RLNC Decoding with Online DAG Scheduling

Simon Wunderlich, Frank H.P. Fitzek, Martin Reisslein

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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 state-of-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).

Original languageEnglish (US)
Article number8891774
Pages (from-to)161184-161200
Number of pages17
JournalIEEE Access
StatePublished - 2019


  • Directed acyclic graph (DAG)
  • helper matrix
  • heterogeneous multicore architecture
  • online scheduling
  • parallel computing
  • random linear network coding (RLNC)

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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