When on-the-fly erasure code makes late video decoding happen

In this paper, we proposed the late decoding solution based on Tetrys (called LD-Tetrys) to deal with delay constrained applications. Our analysis showed that LD-Tetrys fits well to the requirements for late video decoding while the other schemes (e.g., FEC, HARQ) do not. We also developed an evaluation framework which is independent of video codec and network topology. Simulation results acknowledge that LD-Tetrys' performance is better than the normal decoding with Tetrys, the original Tetrys. Furthermore, LD-Tetrys consistently outperforms the traditional block based erasure codes such as AL-FEC in terms of video quality. For future work, we are working on the theoretical modeling and analysis. We also expect to perform extensive experiments to obtain a complete evaluation

Block or Convolutional AL-FEC Codes? A Performance Comparison for Robust Low-Latency Communications

Application-Level Forward Erasure Correction (AL-FEC) codes are a key element of telecommunication systems. They are used to recover from packet losses during large scale content distribution, for instance within the FLUTE/ALC (file transfers) and FECFRAME (continuous real-time media transfers) protocols of the 3GPP Multimedia Broadcast and Multicast Services (MBMS) standard. However currently standardized and deployed AL-FEC codes for these protocols (e.g., Raptor(Q) or LDPC-Staircase) are all block codes which means that the data flow must be segmented into blocks of predefined size. Surprisingly AL-FEC codes based on a sliding encoding window have not yet been considered in spite of their major advantages. This work analyzes both types of codes in the context of real-time (e.g., multimedia) flows. More precisely, it details how to initialize block and convolutional AL-FEC codes to comply with real-time constraints and introduces the " decoding beyond maximum latency " optimization to convolutional codes. Then it compares the added FEC-related latency of both solutions and the decoding throughput of the two codecs. This work highlights the major benefits of convolutional codes for the large scale distribution of real-time flows and supports the idea of extending FECFRAME specifications (RFC 6363) to support convolutional FEC codes