Our paper, titled, "Efficient Mobile Video Streaming via Context-Aware RaptorQ-Based Unequal Error Protection", has been accepted by IEEE Transactions on Multimedia (CCF-B, IF:5.452) for publication.
Abstract:
Mobile video streaming systems typically apply the forward error correction (FEC) at the application layer to cope with packet-level transmission errors, which complements the bitlevel correction mechanisms at the physical layer. However, most existing works fail to exploit the block-level dependencies in both intra- and inter-frame coding modes of a single-layer compressed video, and thus are less efficient for the prevailing H.264/AVC and/or H.265/HEVC compatible single-layer video application.
To this end, we propose a low-complexity FEC, i.e., contextaware RaptorQ (CA-RQ) with unequal error protection (UEP), to improve the error recovery performance of the single-layer mobile video streaming, through incorporating the block-level dependencies in the compressed video data. We use a packetlevel video transmission distortion model that considers the dependencies in both spatial and temporal domains, to quantify the importance of video packets within a group of pictures (GoP). The compressed video packets, are categorized and grouped into several classes according to their importance to construct the CA-RQ code with the UEP property.We provide a theoretical analysis on redundancy allocation bounds to demonstrate the superior performance of proposed CA-RQ over the standard RaptorQ code. In the meantime, extensive simulations have shown that our scheme not only offers much better subjective visual quality with less than 50% additional redundant symbols as compared to the Macroblock-Based UEP (MB-UEP) scheme, but also outperforms the MB-UEP and classical equal error protection (EEP) based schemes, by a 0.45%~5.71% and 0.94%~6.78% margin respectively in reconstructed quality evaluated using the structural similarity (SSIM) index, across a reasonable range of redundancy proportions.
Congrats to Hao Chen!