Channel Efficiency Aware Scheduling Algorithm for Real-Time Services in Wireless Networks

In this paper, we consider the problem of scheduling real time services over time-varying wireless links in broad-band wireless networks where an Adaptive Modulation and Coding (AMC) scheme is applied in the physical layer in order to decrease the packet error rate. It is well known that a properly chosen modulation and coding scheme can increase error robustness in the physical layer. However, this is at the expense of higher system complexity and decreased channel efficiency. We present a novel Near Maximum Weighted Bipartite Matching (NMWBM) scheduling algorithm, which schedules real time services in accordance with delay bounds and phys-cal layer modulation and coding modes. Numerical results set in the context of IEEE 802.16 networks show that NMWBM can improve system packet throughput and pro-vide higher channel efficiency compared to the existing Earliest Deadline First scheduling algorithm. NMWBM provides this improved performance while meeting delay bound and packet loss rate requirements of real time ser-vices in broadband wireless networks

Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines

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 asymptotical 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.Comment: 11 pages, 13 figure

Scheduling and Resource Allocation for SVC Streaming over OFDM Downlink Systems

info:eu-repo/semantics/publishe