Under the assumption that the encoders’ observations are conditionally independent Markov chains given an unobserved time-invariant random variable, results on the structure of optimal real-time encoding and decoding functions are obtained. The problem with noiseless channels and perfect memory at the receiver is then considered. A new methodology to find the structure of optimal real-time encoders is employed. A sufficient statistic with a time-invariant domain is found for this problem. This methodology exploits the presence of common information between the encoders and the receiver when communication is over noiseless channels. In this paper we estimate the lower bond, upper bond and define the encoder. In the previous design approach they follow Markov Chain approach to estimating the upper bound and define the encoder. In this dissertation we follow poison distribution to finding the lower bound and upper bound. Poisson can be viewed as an approximation to the binomial distribution. The approximation is good enough to be useful even when the sample size (N) is only moderately large (say N > 50) and the probability (p) is only relatively small (p < .2) The advantage of the Poisson distribution, of course, is that if N is large you need only know p to determine the approximate distribution of events. With the binomial distribution you also need to know N.