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.
In this paper, we present a novel layered scalable video transmission scheme over multi-input multi-output (MIMO)
wireless systems. The proposed layered scalable video transmission scheme is able to adaptively select the MIMO sub-channels
for prioritized delivery of layered video signals based on only estimated partial channel state information (CSI).
This scheme is fundamentally different from open loop (OL)-MIMO systems such as V-BLAST in which the CSI is only
available at the receiver side. Without CSI at the transmitter, data sequences in OL-MIMO are transmitted
simultaneously with equal power via multiple antennas. Therefore, OL-MIMO systems are not appropriate for
transmitting compressed video data that need prioritized transmission. In this research, we assume that partial CSI, or the
ordering of each sub-channel's SNR strength, is available at the transmitting end through simple estimation and
feedback. The adaptive channel selection (ACS) algorithm we developed in this research shall switch the bit-stream
automatically to match the ordering of SNR strength for the sub-channels. Essentially, we will launch higher priority
layer bit-stream into higher SNR strength sub-channel by the proposed ACS algorithm. In this fashion, we can implicitly
achieve unequal error protection (UEP) for layered scalable video coding transmission over MIMO system.
Experimental results show that the proposed scheme is able to achieve UEP with partial CSI and the reconstructed video
PSNRs demonstrate the performance improvement of the proposed system as compared with OL-MIMO system.
We present in this paper an integrated robust image transmission scheme using space-time block codes (STBC) over
multi-input multi-output (MIMO) wireless systems. First, in order to achieve an excellent error resilient capability,
multiple bitstreams are generated based on wavelet trees along the spatial orientations. The spatial-orientation trees in the
wavelet domain are individually encoded using SPIHT. Error propagation is thus limited within each bitstreams. Then,
Reed-Solomon (R-S) codes as forward error correction (FEC) are adopted to combat transmission errors over error-prone
wireless channels and to detect residual errors so as to avoid error propagation in each bitstream. FEC can reduce the bit
error rates at the expenses of increased data rate. However, it is often difficult to design an optimal FEC scheme for a
time-varying multi-path fading channel that may fluctuate beyond the capacity of the adopted FEC scheme. Therefore, in
order to overcome such difficulty, we propose an approach to alleviate the effect of multi-path fading by employing the
STBC for spatial diversity with assumption that channel state information (CSI) is perfectly estimated at the receiver.
Experimental results demonstrate that the proposed scheme can achieve much improved performance in terms of PSNR
over Rayleigh flat fading channel as compared with a wireless system without spatial diversity.
In this paper, we proposed a multiple description distributed image coding system for mobile wireless transmission. The innovations of proposed system are two folds: First, when MDC is applied to wavelet subband based image coding; it is possible to introduce correlation between the descriptions in each subband. At the encoder, the correlation information is encoded by systematic Reed Solomon (RS) encoder. Only the parity check bits are sent to channel. At the receiver, when part of descriptions are lost, however, their correlation information are available, the Wyner Ziv decoder can still recover the description by using the correlation information and the partly received description as noisy version and the side information. Secondly, in each description, we use multiple bitstream image coding to achieve error robust transmission. In conventional entropy subband coding, the first bit error may cause the decoder to discard all subsequent bits. A multiple bitstream image encoding is developed based on the decomposition of images in the wavelet domain. We show that such decomposition is able to reduce error propagation in transmission, thus to achieve scalability on PSNR performance over the changes of BER, Experimental result shows that the PSNR could be improved with the same coding rate.
Multiple description coding (MDC) is a well-known robust data compression algorithm designed to minimize the distortion caused by data loss in packet-based communication systems. Several MDC schemes for transmitting wavelet compressed images have been developed. However, these MDC schemes cannot be adopted for digital mobile wireless applications where both packet loss and bit error are present, because individual description in these MDC schemes usually does not have adequate error resilience capability to combat the bit error in transmission. In this paper, we propose an algorithm to achieve robust communication over error prone transmission channels with both packet loss and bit error. We integrate the multiple description scalar quantization (MDSQ) with the multiple wavelet tree image coding method in order to provide an excellent error resilient capability. Two descriptions are generated independently by using index assignment of MDSQ. For each description, multiple sub-sampling is applied to split the wavelet coefficients of the source image into multiple sub-sources. Each sub-source is then entropy coded using the SPIHT algorithm and followed by a channel coding scheme that combines cyclic redundancy code (CRC) and rate compatible punctured convolutional (RCPC) code to offer unequal error protection to the entropy coded bits. The unequal error protection channel coding rate is designed based on the bit error sensitivity of different bit planes to achieve maximum end-to-end quality of service. Experimental results show that the proposed scheme not only exhibits an excellent error resilient performance but also demonstrates graceful degradation over error prone channels with changing rate of packet loss and bit error.