Title :
Multiple description coding in networks with congestion problem
Author :
Alasti, Mehdi ; Sayrafian-Pour, Kamran ; Ephremides, Anthony ; Farvardin, Nariman
Author_Institution :
Dept. of Electr. & Comput. Eng., Maryland Univ., College Park, MD, USA
fDate :
3/1/2001 12:00:00 AM
Abstract :
Suppose that the description of a stochastic process needs to be sent to a destination through a communication network. Also assume there is a risk that the description may be lost. A technique to reduce the risk of losing such descriptions is by sending two (or more) descriptions and hoping that in this way at least one of the descriptions will get through. This problem is referred to as multiple description coding (MDC) and was first introduced by Gersho, Witsenhausen, Wolf, Wyner, Ziv and Ozarow (1979). So far, the main focus of research on this subject has been on the achievable rate-distortion functions and the related structural design issues for such encoders and decoders. Little effort has focused on the performance of such coding schemes in simple communication networks and relation of the overall distortion with respect to some network parameter such as congestion. In this paper, a double description coding (DDC) system in a simple network represented by a set of parallel queues is studied. Comparison is made with a single description coding system and it is shown that DDC significantly improves the overall average end-to-end distortion at high network loading
Keywords :
encoding; queueing theory; rate distortion theory; stochastic processes; telecommunication congestion control; telecommunication networks; achievable rate-distortion functions; average end-to-end distortion; communication network; decoders; double description coding; encoders; high network loading; multiple description coding; network congestion; network parameter; parallel queues; performance; single description coding system; stochastic process; structural design; Communication networks; Communication systems; Conferences; Decoding; Government; Information theory; Intelligent networks; Rate-distortion; Signal to noise ratio; Stochastic processes;
Journal_Title :
Information Theory, IEEE Transactions on