Optimized scalable Multiple-Description Coding and FEC-based Joint Source-Channel Coding: A performance comparison

Stoufs, Maryse; Munteanu, Adrian; Barbarien, Joeri; Cornelis, Jan; Schelkens, Peter

doi:10.1109/wiamis.2009.5031435

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Joint source-channel coding. Besides SoftCast and Flex-Cast, there have been a number of designs for lossy singleantenna channels (e.g., [24,30,27]), though few for MIMO. ParCast follows the same motivation to jointly consider source compression and error resilience at the channel, but specifically designs a scheme to leverage the properties of both the source and the MIMO-OFDM channel with a single code.…”

Section: Related Workmentioning

confidence: 99%

ParCast

Liu

et al. 2012

Proceedings of the 18th Annual International Conference on Mobile Computing and Networking

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We observe two trends, growing wireless capability at the physical layer powered by MIMO-OFDM, and growing video traffic as the dominant application traffic. Both the source and MIMO-OFDM channel components exhibit non-uniform energy distribution. This motivates us to leverage the source data redundancy at the channel to achieve high video recovery performance. We propose ParCast that first separates the source and channel into independent components, matches the more important source components with highergain channel components, allocates power weights with joint consideration to the source and the channel, and uses analog modulation for transmission. Such a scheme achieves finegrained unequal error protection across source components. We implemented ParCast in Matlab and on Sora. Extensive evaluation shows that our scheme outperforms competitive schemes by notable margins, sometimes up to 5 dB in PSNR for challenging scenarios.

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Section: Related Workmentioning

confidence: 99%

ParCast

Liu

et al. 2012

Proceedings of the 18th Annual International Conference on Mobile Computing and Networking

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“…However, the generalization of such results to real-world data is not trivial, as MDC performance bounds are known not to be strict [15]. In [16], [17], a joint application of source channel coding and scalable MDC is proposed. From the reported results, worked out using Gaussian data, one can infer that methods using channel coding generally outperform MDC in matched transmission conditions.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Between ULP and MDC With Many Descriptions for Image Transmission

Baccaglini

Tillo

Olmo

2010

IEEE Signal Process. Lett.

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In this letter, we present a performance comparison between multiple description coding (MDC) and unequal loss protection (ULP) for progressive image transmission over lossy packet networks. Two optimization criteria are considered, i.e., a multiquality criterion, when distinct quality levels are guaranteed at the decoder side, and the optimization of the expected quality at the receiver. We resort to both a semi-analytical approach and simulation results. To enable numerical comparisons, we address a specific MDC algorithm suitable for progressive imaging, and a state-of-the-art ULP algorithm based on Reed Solomon codes. The results, although cannot be generalized to any MDC and ULP methods, are useful to put into evidence some general features that can drive the selection of the most proper technique for the application at hand. In fact, they allow to put into evidence the main advantages and drawbacks of either technique.

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Optimized layered integrated video encoding

Yun

Kim

et al. 2015

2015 IEEE Conference on Computer Communications (INFOCOM)

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Optimized scalable Multiple-Description Coding and FEC-based Joint Source-Channel Coding: A performance comparison

Cited by 4 publications

References 7 publications

ParCast

ParCast

A Comparison Between ULP and MDC With Many Descriptions for Image Transmission

Optimized layered integrated video encoding

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