Combined source-channel coding schemes for video transmission over an additive white Gaussian noise channel

Bystrom, M.; Modestino, J.W.

doi:10.1109/49.848242

Cited by 85 publications

(68 citation statements)

References 15 publications

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“…In [8,9], Bystrom and Modestino have demonstrated how this explosive computational burden can be avoided through the use of universal operational rate-distortion characteristics which describe the rate-distortion performance of a selected source coding scheme and includes the effects of channel errors as well as source coding losses. This approach was applied in [8,10], to develop a JSCC methodology for single-layer video transmission over wireless channels modeled as a slow-fading Rician channel.…”

Section: Introductionmentioning

confidence: 99%

Performance of Multilayered Video Encoding and Delivery over Lossy Channels Using a Joint Source-Channel Coding Approach

Pei

Modestino

2006

Wireless Pers Commun

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In this paper, the performance of selected error-control schemes based on forward error-control (FEC) coding for H.263+ video transmission over an additive white Gaussian noise (AWGN) channel is studied. Joint source and channel coding (JSCC) techniques that employ single-layer and 2-layer H.263+ coding in conjunction with unequal error protection (UEP) to combat channel errors are quantitatively compared. Results indicate that with appropriate joint source and channel coding, tailored to the respective layers, FEC-based error control in combination with 2-layer video coding techniques can lead to more acceptable quality for wireless video delivery in the presence of channel impairments.

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Section: Introductionmentioning

confidence: 99%

Performance of Multilayered Video Encoding and Delivery over Lossy Channels Using a Joint Source-Channel Coding Approach

Pei

Modestino

2006

Wireless Pers Commun

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“…Most of the related work in video transmission focus on (1) finding an optimal bit rate for video coding and channel coding, e.g., [19,20]; (2) designing the video coding mechanism to achieve the target source rate under given channel conditions, e.g., [21]; (3) designing the channel coding to achieve the required reliability, e.g., low-density parity check [22], turbo [23], Reed-Solomon (RS) [24], and fountain [25] codes; (4) designing joint optimization framework, including all available error control components together with error concealment and transmission control, to improve global system performance, e.g., [26]. The authors of [14] deal with the optimal allocation of MPEG-2 encoding and media-independent forward error correction rates under the total given bandwidth.…”

Section: Joint Source-channel Codingmentioning

confidence: 99%

Joint source-channel coding and optimization for mobile video streaming in heterogeneous wireless networks

Shang

Huang

et al. 2013

J Wireless Com Network

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This paper investigates mobile video delivery in a heterogeneous wireless network from a video server to a multi-homed client. Joint source-channel coding (JSCC) has proven to be an effective solution for video transmission over bandwidth-limited, error-prone wireless networks. However, one major problem with the existing JSCC approaches is that they consider the network between the server and the client as a single transport link. The situation becomes more complicated in the context of multiple available links because involving a low-bandwidth, highly lossy, or long-delay wireless network in the transmission will only degrade the video quality. To address the critical problem, we propose a novel flow rate allocation-based JSCC (FRA-JSCC) approach that includes three key phases: (1) forward error correction redundancy estimation under loss requirement, (2) source rate adaption under delay constraint, and (3) dynamic rate allocation to minimize end-to-end video distortion. We present a mathematical formulation of JSCC to optimize video quality over multiple wireless channels and provide comprehensive analysis for channel distortion. We evaluate the performance of FRA-JSCC through emulations in Exata and compare it with the existing schemes. Experimental results show that FRA-JSCC outperforms the competing models in improving the video peak signal-to-noise ratio as well as in reducing the end-to-end delay.

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“…Motivated by the results and discussions presented in Section III-B1, Case 2, we seek possibly more efficient solutions to calculate the required a posteriori symbol probability using the probability of a sequence larger than one state, i.e., 4 Note that the computational complexity of the forward equation includes the cost of normalization as well (2M ). This is a common approach in this work in which the forward probabilities of trellis states which are stored to be used in the next time instant are always normalized.…”

Section: ) Alternative Solutionsmentioning

confidence: 99%

“…Examples of the works in this class are present in [2]- [6]. The applications span across the areas of speech, image, and video coding such as that of Modestino et al on image coding using the discrete cosine transform with convolutional channel coding [2], the work of Moore and Gibson on differential pulse-code modulation (DPCM) speech coding with self-orthogonal convolutional coding [3] and the work of Bystrom and Modestino on combined source channel coding for video transmission [4].…”

Section: Introductionmentioning

confidence: 99%

Efficient Source Decoding Over Memoryless Noisy Channels Using Higher Order Markov Models

Lahouti

Khandani

2004

IEEE Trans. Inform. Theory

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Abstract-Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth-efficient way to combat noisy channel degradations. This redundancy can be employed to either assist the channel decoder for improved performance or design better source decoders. In this work, a family of solutions for the asymptotically optimum minimum mean-squared error (MMSE) reconstruction of a source over memoryless noisy channels is presented when the redundancy in the source encoder output stream is exploited in the form of a -order Markov model ( 1) and a delay of 0 is allowed in the decoding process. It is demonstrated that the proposed solutions provide a wealth of tradeoffs between computational complexity and the memory requirements. A simplified MMSE decoder which is optimized to minimize the computational complexity is also presented. Considering the same problem setup, several other maximum a posteriori probability (MAP) symbol and sequence decoders are presented as well. Numerical results are presented which demonstrate the efficiency of the proposed algorithms.Index Terms-Forward-backward recursion, joint sourcechannel coding, maximum a posteriori probability (MAP) detection, Markov sources, minimum mean-squared error (MMSE) estimation, residual redundancies, source decoding.

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Combined source-channel coding schemes for video transmission over an additive white Gaussian noise channel

Cited by 85 publications

References 15 publications

Performance of Multilayered Video Encoding and Delivery over Lossy Channels Using a Joint Source-Channel Coding Approach

Performance of Multilayered Video Encoding and Delivery over Lossy Channels Using a Joint Source-Channel Coding Approach

Joint source-channel coding and optimization for mobile video streaming in heterogeneous wireless networks

Efficient Source Decoding Over Memoryless Noisy Channels Using Higher Order Markov Models

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