A network
system providing highly
reliable transmission quality for
delay-sensitive applications with
online learning and cross-layer optimization is disclosed. Each protocol layer is deployed to select its own optimization strategies, and cooperates with other
layers to maximize the overall utility. This framework adheres to defined layered
network architecture, allows
layers to determine their own protocol parameters, and exchange only limited information with other
layers. The network
system considers heterogeneous and dynamically changing characteristics of
delay-sensitive applications and the underlying time-varying
network conditions, to perform cross-layer optimization. Data units (DUs), both independently decodable DUs and interdependent DUs, are considered. The optimization considers how the cross-layer strategies selected for one DU will
impact its neighboring DUs and the DUs that depend on it. While attributes of future DU and
network conditions may be unknown in real-time applications, the
impact of current cross-layer actions on 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, the network
system utilizes a low-complexity cross-layer optimization
algorithm using
online learning for each DU transmission.