Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Convex Optimization
Resource allocation and cross-layer control in wireless networks
Foundations and Trends® in Networking
Delay with network coding and feedback
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
On broadcast stability of queue-based dynamic network coding over erasure channels
IEEE Transactions on Information Theory
Joint scheduling and instantaneously decodable network coding
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Scheduling heterogeneous real-time traffic over fading wireless channels
INFOCOM'10 Proceedings of the 29th conference on Information communications
Stochastic Network Optimization with Application to Communication and Queueing Systems
Stochastic Network Optimization with Application to Communication and Queueing Systems
Adaptive network coding for scheduling real-time traffic with hard deadlines
Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing
IEEE Transactions on Information Theory
A Random Linear Network Coding Approach to Multicast
IEEE Transactions on Information Theory
On the Delay and Throughput Gains of Coding in Unreliable Networks
IEEE Transactions on Information Theory
Adaptive network coding for scheduling real-time traffic with hard deadlines
Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing
Hi-index | 0.00 |
We study adaptive network coding (NC) for scheduling real-time traffic over a single-hop wireless network. To meet the hard deadlines of real-time traffic, it is critical to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. Thus motivated, we explore adaptive NC, where the block size is adapted based on the remaining time to the deadline, by casting this sequential block size adaptation problem as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and the optimal block size is bounded by the "greedy" block size. These unique structures make it possible to narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can solve for the optimal block size in polynomial time. Since channel erasure probabilities would be time-varying in a mobile network, we further develop a joint real-time scheduling and channel learning scheme with adaptive NC that can adapt to channel dynamics. We also generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints, devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotic throughput-optimality. In addition to analysis and simulation results, we perform high fidelity wireless emulation tests with real radio transmissions to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.