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Several types of AL-FEC (Application-Level FEC) codes for the Packet ErasureChannel exist. Random Linear Codes (RLC), where redundancy packets consist ofrandom linear combinations of source packets over a certain finite field, are asimple yet efficient coding technique, for instance massively used for NetworkCoding applications. However the price to pay is a high encoding and decodingcomplexity, especially when working on $GF(2^8)$, which seriously limits thenumber of packets in the encoding window. On the opposite, structured blockcodes have been designed for situations where the set of source packets isknown in advance, for instance with file transfer applications. Here theencoding and decoding complexity is controlled, even for huge block sizes,thanks to the sparse nature of the code and advanced decoding techniques thatexploit this sparseness (e.g., Structured Gaussian Elimination). But theirdesign also prevents their use in convolutional use-cases featuring an encodingwindow that slides over a continuous set of incoming packets.  In this work we try to bridge the gap between these two code classes,bringing some structure to RLC codes in order to enlarge the use-cases wherethey can be efficiently used: in convolutional mode (as any RLC code), but alsoin block mode with either tiny, medium or large block sizes. We alsodemonstrate how to design compact signaling for these codes (forencoder/decoder synchronization), which is an essential practical aspect.

Structured Random Linear Codes (SRLC): Bridging the Gap between Block and Convolutional Codes