  We consider a system in which two nodes take correlated measurements of a
random source with time-varying and unknown statistics. The observations of the
source at the first node are to be losslessly replicated with a given
probability of outage at the second node, which receives data from the first
node over a constant-rate errorless channel. We develop a system and associated
strategies for joint distributed source coding (encoding and decoding) and
transmission control in order to achieve low end-to-end delay. Slepian-Wolf
coding in its traditional form cannot be applied in our scenario, since the
encoder requires the joint statistics of the observations and the associated
decoding delay is very high. We analytically evaluate the performance of our
strategies and show that the delay achieved by them are order optimal, as the
conditional entropy of the source approaches to the channel rate. We also
evaluate the performance of our algorithms based on real-world experiments
using two cameras recording videos of a scene at different angles. Having
realized our schemes, we demonstrated that, even with a very low-complexity
quantizer, a compression ratio of approximately 50% is achievable for lossless
replication at the decoder, at an average delay of a few seconds.
