  Cross-layer optimization solutions have been proposed in recent years to
improve the performance of network users operating in a time-varying,
error-prone wireless environment. However, these solutions often rely on ad-hoc
optimization approaches, which ignore the different environmental dynamics
experienced at various layers by a user and violate the layered network
architecture of the protocol stack by requiring layers to provide access to
their internal protocol parameters to other layers. This paper presents a new
theoretic foundation for cross-layer optimization, which allows each layer to
make autonomous decisions individually, while maximizing the utility of the
wireless user by optimally determining what information needs to be exchanged
among layers. Hence, this cross-layer framework does not change the current
layered architecture. Specifically, because the wireless user interacts with
the environment at various layers of the protocol stack, the cross-layer
optimization problem is formulated as a layered Markov decision process (MDP)
in which each layer adapts its own protocol parameters and exchanges
information (messages) with other layers in order to cooperatively maximize the
performance of the wireless user. The message exchange mechanism for
determining the optimal cross-layer transmission strategies has been designed
for both off-line optimization and on-line dynamic adaptation. We also show
that many existing cross-layer optimization algorithms can be formulated as
simplified, sub-optimal, versions of our layered MDP framework.
