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In this paper, we propose a general cross-layer optimization framework inwhich we explicitly consider both the heterogeneous and dynamically changingcharacteristics of delay-sensitive applications and the underlying time-varyingnetwork conditions. We consider both the independently decodable data units(DUs, e.g. packets) and the interdependent DUs whose dependencies are capturedby a directed acyclic graph (DAG). We first formulate the cross-layer design asa non-linear constrained optimization problem by assuming complete knowledge ofthe application characteristics and the underlying network conditions. Theconstrained cross-layer optimization is decomposed into several cross-layeroptimization subproblems for each DU and two master problems. The proposeddecomposition method determines the necessary message exchanges between layersfor achieving the optimal cross-layer solution. However, the attributes (e.g.distortion impact, delay deadline etc) of future DUs as well as the networkconditions are often unknown in the considered real-time applications. Theimpact of current cross-layer actions on the future DUs can be characterized bya state-value function in the Markov decision process (MDP) framework. Based onthe dynamic programming solution to the MDP, we develop a low-complexitycross-layer optimization algorithm using online learning for each DUtransmission. This online algorithm can be implemented in real-time in order tocope with unknown source characteristics, network dynamics and resourceconstraints. Our numerical results demonstrate the efficiency of the proposedonline algorithm.

Decomposition Principles and Online Learning in Cross-Layer Optimization for Delay-Sensitive Applications