High Performance Multiple Description Code Design

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In the multiple description (MD) problem, the goal is to generate a given number, say K, of bitstreams (i.e., descriptions) representing a signal, such that each bitstream can be decoded to an acceptable quality, while more bitstreams refine each other improving the signal reconstruction. MD coding finds applications in transmission of multimedia signals over packet lossy (wired or wireless) networks as it ensures graceful recovery from losses when retransmission is not an option due to stringent delay constraints. Other applications are in diversity communications systems, distributed data storage and multirate multicast. Most practical MD codes address the case of two descriptions, while practical designs for general K are scarce and their performance is some distance from the theoretical optimum. On the other hand, while traditionally MD codes were designed to combat description loss, their ability to combat bit errors has emerged as a side product. This feature is especially useful in wireless communications. However, the design of MD codes to take advantage of this property has received little attention. The purpose of the proposed research is to develop high performance MD codes with increased capability to recover from losses and bit errors. This will be achieved through MD designs of high flexibility in controlling the trade-off between the quality of reconstruction for different subsets of received descriptions and through construction of index assignments with good Hamming distance properties. An important component of this research will be the training of highly qualified personnel. Several graduate students will be engaged in this research and will be trained in multimedia coding and communications, information theory and optimization; training that will prepare them for an engineering or academic career in the information technology sector. The proposed research will advance the knowledge and understanding in the area of MD code design and the techniques and insights developed will be also applicable to other source network coding problems (for instance, in robust distributed source coding).