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

In this paper, we propose a general cross-layer optimization framework for delay-sensitive applications over single wireless links in which we explicitly consider both the heterogeneous and dynamically changing characteristics (e.g., delay deadlines, dependencies, distortion impacts, etc.) of delay-sensitive applications and the underlying time-varying channel conditions. We first formulate this problem as a nonlinear constrained optimization by assuming complete knowledge of the application characteristics and the underlying channel conditions. This constrained cross-layer optimization is then decomposed into several subproblems, each corresponding to the cross-layer optimization for one DU. The proposed decomposition method explicitly considers how the cross-layer strategies selected for one DU will impact its neighboring DUs as well as the DUs that depend on it through the resource price (associated with the resource constraint) and neighboring impact factors (associated with the scheduling constraints). However, the attributes (e.g., distortion impact, delay deadline, etc.) of future DUs as well as the channel conditions are often unknown in the considered real-time applications. In this case, the cross-layer optimization is formulated as a constrained Markov decision process (MDP) in which the impact of current cross-layer actions on the future DUs can be characterized by a state-value function. We then develop a low-complexity cross-layer optimization algorithm using online learning for each DU transmission. This online optimization utilizes information only about the previous transmitted DUs and past experienced channel conditions, which can be easily implemented in real-time in order to cope with unknown source characteristics, channel dynamics and resource constraints. Our numerical results demonstrate the efficiency of the proposed online algorithm.