Optimal 4G OFDMA Dynamic Subcarrier and Power Auction-based Allocation towards H.264 Scalable Video Transmission

In this paper, authors presented a price maximization scheme for optimal orthogonal frequency division for multiple access (OFDMA) subcarrier allocation for wireless video unicast/multicast scenarios. They formulate a pricing based video utility function for H.264 based wireless scalable video streaming, thereby achieving a trade-off between price and QoS fairness. These parametric models for scalable video rate and quality characterization arederived from the standard JSVM reference codec for the SVC extension of the H.264/AVC, and hence are directly applicable in practical wireless scenarios. With the aid of these models, they proposed auction based framework for revenue maximization of the transmitted video streams in the unicast and multicast 4G scenario. A closedform expression is derived for the optimal scalable video quantization step-size subject to the constraints of theunicast/multicast users in 4G wireless systems. This yields the optimal OFDMA subcarrier allocation for multi-userscalable video multiplexing. The proposed scheme is cognizant of the user modulation and code rate, and is henceamenable to adaptive modulation and coding (AMC) feature of 4G wireless networks. Further, they also consider aframework for optimal power allocation based on a novel revenue maximization scheme in OFDMA based wireless broadband 4G systems employing auction bidding models. This is formulated as a constrained convex optimization problem towards sum video utility maximization. We observe that as the demand for a video stream increases inbroadcast/multicast scenarios, higher power is allocated to the corresponding video stream leading to a gain in the overall revenue/utility. We simulate a standard WiMAX based 4G video transmission scenario to validate the performance of the proposed optimal 4G scalable video resource allocation schemes. Simulations illustrate that the proposed optimal band width and power allocation schemes result in a significant performance improvement over the suboptimal equal resource allocation schemes for scalable video transmission.Defence Science Journal, 2013, 63(1), pp.15-24, DOI:http://dx.doi.org/10.14429/dsj.63.375

Scheduling for Multi-Camera Surveillance in LTE Networks

Wireless surveillance in cellular networks has become increasingly important, while commercial LTE surveillance cameras are also available nowadays. Nevertheless, most scheduling algorithms in the literature are throughput, fairness, or profit-based approaches, which are not suitable for wireless surveillance. In this paper, therefore, we explore the resource allocation problem for a multi-camera surveillance system in 3GPP Long Term Evolution (LTE) uplink (UL) networks. We minimize the number of allocated resource blocks (RBs) while guaranteeing the coverage requirement for surveillance systems in LTE UL networks. Specifically, we formulate the Camera Set Resource Allocation Problem (CSRAP) and prove that the problem is NP-Hard. We then propose an Integer Linear Programming formulation for general cases to find the optimal solution. Moreover, we present a baseline algorithm and devise an approximation algorithm to solve the problem. Simulation results based on a real surveillance map and synthetic datasets manifest that the number of allocated RBs can be effectively reduced compared to the existing approach for LTE networks.Comment: 9 pages, 10 figure

An intelligent fuzzy logic-based content and channel aware downlink scheduler for scalable video over OFDMA wireless systems

The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over OFDMA channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station