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