C-DMRC: Compressive Distortion-Minimizing Rate Control for Wireless Multimedia Sensor Networks

Pudlewski, Scott; Melodia, Tommaso; Prasanna, Arvind

doi:10.1109/secon.2010.5508253

Cited by 11 publications

(9 citation statements)

References 36 publications

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“…However, the key problem of enabling real-time quality-aware video streaming in large-scale multihop wireless networks of embedded devices is still open and largely unexplored. There are two key shortcomings in systems based on sending predictively encoded video (e.g., MPEG-4 1 A preliminary shorter version of this paper [1] Part 2, H.264/AVC [5], [6], [7], H.264/SVC [8]) through a layered wireless communication protocol stack, i.e., encoder complexity and low resiliency to channel errors [9].…”

Section: Introductionmentioning

confidence: 99%

Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks

Pudlewski

Prasanna

Melodia

2012

IEEE Trans. on Mobile Comput.

153

120

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Abstract-This article1 presents the design of a networked system for joint compression, rate control and error correction of video over resource-constrained embedded devices based on the theory of compressed sensing. The objective of this work is to design a cross-layer system that jointly controls the video encoding rate, the transmission rate, and the channel coding rate to maximize the received video quality. First, compressed sensing based video encoding for transmission over wireless multimedia sensor networks (WMSNs) is studied. It is shown that compressed sensing can overcome many of the current problems of video over WMSNs, primarily encoder complexity and low resiliency to channel errors. A rate controller is then developed with the objective of maintaining fairness among video streams while maximizing the received video quality. It is shown that the rate of compressed sensed video can be predictably controlled by varying only the compressed sensing sampling rate. It is then shown that the developed rate controller can be interpreted as the iterative solution to a convex optimization problem representing the optimization of the rate allocation across the network. The error resiliency properties of compressed sensed images and videos are then studied, and an optimal error detection and correction scheme is presented for video transmission over lossy channels. Finally, the entire system is evaluated through simulation and testbed evaluation. The rate controller is shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality. Testbed results also show that the rates converge to stable values in real channels.

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Section: Introductionmentioning

confidence: 99%

Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks

Pudlewski

Prasanna

Melodia

2012

IEEE Trans. on Mobile Comput.

153

120

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“…With respect to image and video transmission, [12] designed a video encoder based on CS and a streaming protocol for wireless video transmission. To exploit temporal redundancy, the difference frame of the I frame and the target frame is compressive sensed in [12].…”

Section: B Related Workmentioning

confidence: 99%

“…To exploit temporal redundancy, the difference frame of the I frame and the target frame is compressive sensed in [12]. This means the original video frame is used as the reference frame in the encoder side, while the decoder uses the recovered image as the reference frame.…”

Section: B Related Workmentioning

confidence: 99%

“…eliminate the corresponding rows in the measurement matrix and estimated the quantization error ∆ again in the decoder side. For SVCCS, we compare our proposed GOP structure with that described in [12], i.e., the whole compressive sensed I frame is served as a reference frame. Assume that the DCT coefficients are received correctly.…”

Section: A Performance Of Svccsmentioning

confidence: 99%

“…7 shows the comparison. Since the reference frame in [12] cannot be recovered exactly the same as in the encoder, the decoded video quality is thus degraded. If the measurements of the reference frame are lost, the degradation is more apparent.…”

Section: A Performance Of Svccsmentioning

confidence: 99%

See 2 more Smart Citations

Scalable Video Coding with Compressive Sensing for Wireless Videocast

Xiang

Cai

2011

2011 IEEE International Conference on Communications (ICC)

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Abstract-Channel coding such as Reed-Solomon (RS) and convolutional codes has been widely used to protect video transmission in wireless networks. However, this type of channel coding can effectively correct error bits only if the error rate is smaller than a given threshold; when the bit error rate is underestimated, the effectiveness of channel coding drops dramatically and so does the decoded video quality. In this paper, we propose a low-complex, scalable video coding architecture based on compressive sensing (SVCCS) for wireless unicast and multicast transmissions. SVCCS achieves good scalability, error resilience and coding efficiency. SVCCS encoded bitstream is divided into base and enhancement layers. The layered structure provides quality and temporal scalability. While in the enhancement layer, the CS measurements provide fine granular quality scalability. In addition, we incorporate the state-of-the-art technologies to improve the compressive sensing coding efficiency. Experimental results show that SVCCS is more effective and efficient for wireless videocast than the existing solutions.

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An Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks

Wang

2017

Int J Wireless Inf Networks

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C-DMRC: Compressive Distortion-Minimizing Rate Control for Wireless Multimedia Sensor Networks

Cited by 11 publications

References 36 publications

Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks

Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks

Scalable Video Coding with Compressive Sensing for Wireless Videocast

An Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks

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