In this paper, we address the problem of real-time video streaming over wireless LANs for both unicast and multicast transmission. The wireless channel is modeled as a packet-erasure channel at the IP level. For the unicast scenario, we describe a novel hybrid Automatic Repeat reQuest (ARQ) algorithm that efficiently combines forward error control (FEC) coding with the ARQ protocol. For the multiple-users scenario, we formulate the problem of real-time video multicast as an optimization of a maximum regret cost function across the multicast user space. The proposed solution efficiently combines progressive source coding with FEC coding. We present a theoretical analysis of the unicast and multicast cases, as well as experimental results that demonstrate the performance advantages of the proposed algorithms over existing methods.
With the advent and proliferation of digital cameras and computers, the number of digital photos created and stored by consumers has grown extremely large. This created increasing demand for image retrieval systems to ease interaction between consumers and personal media content. Active learning is a widely used user interaction model for retrieval systems, which learns the query concept by asking users to label a number of images at each iteration. In this paper, we study sampling strategies for active learning in personal photo retrieval. In order to reduce human annotation efforts in a content-based image retrieval setting, we propose using multiple sampling criteria for active learning: informativeness, diversity and representativeness. Our experimental results show that by combining multiple sampling criteria in active learning, the performance of personal photo retrieval system can be significantly improved.
We explore joint source-channel coding (JSCC) for time-varying channels using a multiresolution framework for both source coding and transmission via novel multiresolution modulation constellations. We consider the problem of still image transmission over time-varying channels with the channel state information (CSI) available at 1) receiver only and 2) both transmitter and receiver being informed about the state of the channel, and we quantify the effect of CSI availability on the performance. Our source model is based on the wavelet image decomposition, which generates a collection of subbands modeled by the family of generalized Gaussian distributions. We describe an algorithm that jointly optimizes the design of the multiresolution source codebook, the multiresolution constellation, and the decoding strategy of optimally matching the source resolution and signal constellation resolution "trees" in accordance with the time-varying channel and show how this leads to improved performance over existing methods. The realtime operation needs only table lookups. Our results based on a wavelet image representation show that our multiresolutionbased optimized system attains gains on the order of 2 dB in the reconstructed image quality over single-resolution systems using channel optimized source coding.
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