Name
Title | ||
|---|---|---|
Efficient Mobile Video Streaming via Context-Aware RaptorQ-Based Unequal Error Protection |
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 bit-level correction mechanisms at the physical layer. However, most existing works fail to exploit the block-level dependencies in both intra and interframe 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., context-aware 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 packet-level 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%
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5.71% and 0.94%
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6.78% margin, respectively, in reconstructed quality evaluated using the structural similarity (SSIM) index, across a reasonable range of redundancy proportions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TMM.2019.2928497 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Mobile video streaming,context-aware,raptorq,UEP,FEC | Computer vision,Computer science,Video streaming,Artificial intelligence,Multimedia | Journal |
Volume | Issue | ISSN |
22 | 2 | 1520-9210 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
5 | ||