This paper shows how the priority encoding transmission (PET) framework may be leveraged to exploit both unequal error protection and limited retransmission for RD-optimized delivery of streaming media. Previous work on scalable media protection with PET has largely ignored the possibility of retransmission. Conversely, the PET framework has not been harnessed by the substantial body of previous work on RD optimized hybrid forward error correction/automatic repeat request schemes. We limit our attention to sources which can be modeled as independently compressed frames (e.g., video frames), where each element in the scalable representation of each frame can be transmitted in one or both of two transmission slots. An optimization algorithm determines the level of protection which should be assigned to each element in each slot, subject to transmission bandwidth constraints. To balance the protection assigned to elements which are being transmitted for the first time with those which are being retransmitted, the proposed algorithm formulates a collection of hypotheses concerning its own behavior in future transmission slots. We show how the PET framework allows for a decoupled optimization algorithm with only modest complexity. Experimental results obtained with Motion JPEG2000 compressed video demonstrate that substantial performance benefits can be obtained using the proposed framework.

This paper is concerned with the transmission of scalably compressed data sources over lossy channels. Specifically, this paper is concerned with packet networks or, more generally, erasure channels. Previous work has generally assumed that the source elements form linear dependencies. The contribution of this paper is an unequal erasure protection algorithm which is able to take advantage of scalable data with more general dependency structures. In particular, the proposed scheme is adapted to data with tree-structured dependencies. The source elements are allocated to clusters of packets according to their dependency structure, subject to constraints on packet size and channel code-word length. Given a packet cluster arrangement, source elements are assigned optimal channel codes subject to a constraint on the total transmission length. Experimental results confirm the benefit associated with exploiting the actual dependency structure of the data.

This paper is concerned with the efficient transmission of scalable compressed images with complex dependency structures over lossy communication channels. Our recent work proposed a strategy for allocating source elements into clusters of packets and finding their optimal code rates. However, the previous work assumes that source elements form a simple chain of dependencies. The present paper proposes a modification to the earlier strategy to exploit the properties of scalable sources which have tree-structured dependency. The source elements are allocated to clusters of packets according to their dependency structure, subject to constraints on packet size and channel codeword length. Given a packet cluster arrangement, the proposed strategy assigns optimal code rates to the source elements, subject to a constraint on transmission length. Our experimental results suggest that the proposed strategy can outperform the earlier strategy by exploiting the dependency structure.

This paper is concerned with the efficient transmission of scalable compressed images over lossy communication channels. Recent works have proposed several strategies for assigning optimal code-rates to elements in a scalable data stream, under the assumption that all elements are encoded onto a common group of network packets. When the size of the data to be encoded becomes large in comparison with the size of the network packets, such schemes require very long channel codes with high computational complexity. In networks with high loss, small packets are generally more desirable than long packets. This paper proposes a strategy for assigning optimal code-rates to elements of the scalable compressed data source, subject to constraints on the packet size, transmission length and code complexity. Our experimental results suggest that the proposed scheme can outperform previously proposed code-rate assignment policies, subject to the above-mentioned constraints, particularly with high loss.