Dynamic Data Compression with Distortion Constraints for Wireless   Transmission over a Fading Channel
  We consider a wireless node that randomly receives data from different sensor units. The arriving data must be compressed, stored, and transmitted over a wireless link, where both the compression and transmission operations consume power. Specifically, the controller must choose from one of multiple compression options every timeslot. Each option requires a different amount of power and has different compression ratio properties. Further, the wireless link has potentially time-varying channels, and transmission rates depend on current channel states and transmission power allocations. We design a dynamic algorithm for joint compression and transmission, and prove that it comes arbitrarily close to minimizing average power expenditure, with an explicit tradeoff in average delay. Our approach uses stochastic network optimization together with a concept of place holder bits to provide efficient energy-delay performance. The algorithm is simple to implement and does not require knowledge of probability distributions for packet arrivals or channel states. Extensions that treat distortion constraints are also considered. 
Michael J. Neely and Abhishek Sharma