We have observed two trends, growing wireless capability at the physical layer powered by MIMO-OFDM and growing video traffic as the dominant application traffic. Both the video source and MIMO-OFDM channel components exhibit nonuniform energy distribution. This has motivated us to leverage the source data redundancy at the channel to achieve high video recovery performance. We propose ParCast+ that first separates the source and the channel into independent components, matches the more important source components with higher-gain channel components, allocates power weights with joint consideration to the source and the channel, and uses pseudo-analog modulation for transmission. Such a scheme achieves fine-grained unequal error protection across source components. We implemented ParCast+ in Matlab and on Sora. Extensive evaluation has shown that our scheme outperforms competing schemes by notable margins, sometimes up to 6.4 dB in PSNR for challenging scenarios.

The key challenge in a broadcast system is receiver heterogeneity, where the weakest receiver typically constrains the entire system performance. Traditionally, this arises from heterogeneous channel SNRs at different receivers. In Multiple-Input Multiple-Output (MIMO) systems, a further heterogeneity is caused by different antenna numbers across receivers. We propose a compressive broadcast framework to address both types of heterogeneity. By layering compressive sensing (CS) over MIMO transmissions, our framework ensures a received source quality commensurate with the channel SNR and the MIMO channel dimension. Compared with a conventional framework, our framework achieves more smooth rate increase with channel SNR and much higher performance for multi-antenna receivers.

We observe two trends, growing wireless capability at the physical layer powered by MIMO-OFDM, and growing video traffic as the dominant application traffic. Both the source and MIMO-OFDM channel components exhibit non-uniform energy distribution. This motivates us to leverage the source data redundancy at the channel to achieve high video recovery performance. We propose ParCast that first separates the source and channel into independent components, matches the more important source components with higher-gain channel components, allocates power weights with joint consideration to the source and the channel, and uses analog modulation for transmission. Such a scheme achieves fine-grained unequal error protection across source components. We implemented ParCast in Matlab and on Sora. Extensive evaluation shows that our scheme outperforms competitive schemes by notable margins, sometimes up to 5~dB in PSNR for challenging scenarios.

This paper mathematically analyzes the verification based message passing decoder for the binary compressive sensing (CS) compression problem, toward the goal of judging whether the decoding will be successful prior to the decoding process and of providing design guidelines of sensing matrices for better coding performance. For a biased binary source with unequal percentage of bit "0" and "1", we observe that the recovery probabilities for bit "0" and "1" are different during each round of evolution in the decoding process. We refer to this property as asymmetrical recovery. To authors' best knowledge, this paper is the first one to formulate CS decoding by taking the asymmetrical recovery into account. With the new formulation, the recursion of unrecoverable probability of source bits can be characterized more precisely than previous formulations. Furthermore, as an important property of CS decoding, we also characterize the variation on asymmetrical recovery in different rounds of evolution. The simulation results show that the new formulation has a close match with the actual decoding. Finally, we derive design guidelines for random sensing matrices from the source coding perspective.

In this paper, we present a study on the visual navigation for a mobile robot. We perform the boundary detecting and color cluster techniques respectively to segment the road and nonroad. We propose a conception about useful information to decrease the computation, and compose the information with a special color coordinate base and the geometrical feature into a hybrid feature space to improve the color clustering.>