  We propose two scheduling algorithms that seek to optimize the quality of
scalably coded videos that have been stored at a video server before
transmission.} The first scheduling algorithm is derived from a Markov Decision
Process (MDP) formulation developed here. We model the dynamics of the channel
as a Markov chain and reduce the problem of dynamic video scheduling to a
tractable Markov decision problem over a finite state space. Based on the MDP
formulation, a near-optimal scheduling policy is computed that minimize the
mean square error. Using insights taken from the development of the optimal
MDP-based scheduling policy, the second proposed scheduling algorithm is an
online scheduling method that only requires easily measurable knowledge of the
channel dynamics, and is thus viable in practice. Simulation results show that
the performance of both scheduling algorithms is close to a performance upper
bound also derived in this paper.
