Transmission of Progressive Images Over Noisy Channels: An End-to-End Statistical Optimization Framework

We present a statistical optimization framework for solving the end-to-end problem of multiple antenna transmission of progressive images over noisy channels. Such channels exhibit temporally correlated loss characteristics and are associated with wireless communication links. In our study,we protect the progressive bitstream associated with an image source utilizing a family of rate compatible punctured Reed–Solomon (RS) product codes along with receiver feedback. We consider the impacts of transmission bit errors as well as packet erasures. To cope with the impact of random bit errors, we formulate an optimization problem aimed at minimizing the end-to-end expected distortion of a reconstructed image subject to rate and efficiency constraints. In order to eliminate the impact of packet erasures, we propose utilizing an algorithm that is capable of statistically guaranteeing the delivery of a number of packet sets associated with a progressive bitstream. Our experiments capture the effects of embedding multiple antennas in the transmission of progressive images over wireless tandem channels. Under identical power constraints, our results show that increasing the number of antennas on either transmitting or receiving sides improves the quality of a reconstructed image. Further, the use of receive diversity used in conjunction with simple communication coding schemes such as Maximum Ratio Combining (MRC) yields more improvements than the use of transmit diversity used in conjunction with comparable communication coding schemes such as Space–Time Block Code (STBC). Finally, the use of receiver feedback can further improve the quality of an image reconstructed in the absence of feedback