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Johnson Thie

Publications and source records attributed to Johnson Thie.

2 recordsLinked to original sources

Optimal erasure protection for scalably compressed video streams with limited retransmission.

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.

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Optimal erasure protection strategy for scalably compressed data with tree-structured dependencies.

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.

Algorithms↗