In this paper, a wireless channel is viewed as a heterogeneous network in the time domain, and an adaptive video transmission scheme for H.264 scalable video over wireless channels modeled as a finite-state Markov chain processes is presented. In order to investigate the robustness of adaptive video transmission for H.264 scalable video over wireless channels, statistical channel models can be employed to characterize the error and loss behavior of the video transmission. Among various statistical channel models, a
finite-state Markov model has been considered as suitable for both wireless links as Rayleigh fading channels and wireless local area networks as a combination of bit errors and packet losses. The H.264 scalable video coding enables the rate adaptive source coding and the feedback of channel parameters facilitates the adaptive channel coding based on the dynamics of the channel behavior. As a result, we are able to develop a true adaptive joint source and channel based on instantaneous channel estimation feedback. Preliminary experimental results demonstrate that the estimation of the finite-state Markov channel can be quite accurate and the adaptive video transmission based on channel estimation is able to perform significantly better than the simple channel model in which only average bit error rate is used for joint source and channel coding design.
KEYWORDS: Computer programming, Video coding, Video, Error control coding, Data compression, Video compression, Motion estimation, Video surveillance, Error analysis, Forward error correction
In this paper, we present a distributed video coding scheme based on zero motion identification at the decoder and constrained rate adaptive low density parity check (LDPC) codes. Zero-motion-block identification mechanism is introduced at the decoder, which takes the characters of video sequence into account. The constrained error control decoder can use the bits in the zero motion blocks as a constraint to achieve a better decoding performance and further improve the overall video compression efficiency. It is only at the decoder side that the proposed scheme exploits temporal and spatial redundancy without introducing any additional processing at the encoder side, which keeps the complexity of the encoding as low as possible with certain compression efficiency. As a powerful alternative to Turbo codes, LDPC codes have been applied to our scheme. Since video data are highly non-ergodic, we use rate-adaptive LDPC codes to fit this variation of the achievable compression rate in our scheme. We propose a constrained LDPC decoder not only to improve the decoder efficiency but also to speed the convergence of the iterative decoding. Simulation demonstrates that the scheme has significant improvement in the performances. In addition, the proposed constrained LDPC decoder may benefit other application.
KEYWORDS: Sensor networks, Data compression, Sensors, Error control coding, Computer programming, Data communications, Forward error correction, Wireless communications, Energy efficiency, Data transmission
One of the most important design issues in wireless sensor networks is energy efficiency. Data aggregation has
significant impact on the energy efficiency of the wireless sensor networks. With massive deployment of sensor nodes
and limited energy supply, data aggregation has been considered as an essential paradigm for data collection in sensor
networks. Recently, distributed source coding has been demonstrated to possess several advantages in data aggregation
for wireless sensor networks. Distributed source coding is able to encode sensor data with lower bit rate without direct
communication among sensor nodes. To ensure reliable and high throughput transmission with the aggregated data, we
proposed in this research a progressive transmission and decoding of Rate-Compatible Punctured Convolutional (RCPC)
coded data aggregation with distributed source coding. Our proposed 1/2 RSC codes with Viterbi algorithm for
distributed source coding are able to guarantee that, even without any correlation between the data, the decoder can
always decode the data correctly without wasting energy. The proposed approach achieves two aspects in adaptive data
aggregation for wireless sensor networks. First, the RCPC coding facilitates adaptive compression corresponding to the
correlation of the sensor data. When the data correlation is high, higher compression ration can be achieved. Otherwise,
lower compression ratio will be achieved. Second, the data aggregation is adaptively accumulated. There is no waste of
energy in the transmission; even there is no correlation among the data, the energy consumed is at the same level as raw
data collection. Experimental results have shown that the proposed distributed data aggregation based on RCPC is able to
achieve high throughput and low energy consumption data collection for wireless sensor networks
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