Multi-Source Video Transmission with Minimized Total Distortion Over Wireless Ad Hoc Networks

Abstract

Many optimization theoretic based rate allocation strategies have been developed for allocating some optimal rates to the competing users in wireless ad hoc networks. By considering different objective functions (such as congestion level, total packet loss and so on), the researchers propose some optimization framework by which the problem can be solved. Due to the rapid increase in the development of different video applications in such environments and the existence of difficulties in satisfying the pre-specified QoS limits, increasing the received video quality can be considered as an important and challenging issue. The quality of the received video stream is inversely proportional to the amount of distortion which is being imposed on the video stream by the network packet loss and the video encoder. The main objective of the current paper is to introduce an optimization framework in which by optimal rate allocation to some competing video sources, the aggregate distortion of the all of the sources be minimized. The simulation results verify the claims.

Enabling Differentiated QoS Based on Cross-Layer Optimization in Wireless Ad Hoc Networks

“…Proof: Please refer to [12]. Now, we propose an theorem based on which, the existence and uniqueness of the solution vector of the system (12-14) can be proved.…”

“…Proof: Please refer to [12]. Theorem 3: Assume M jk −1 > 1 and consider the general form of the theorem 2 in which there exists the possibility of multiple congested links in path j of the source k. Based on the assumption (17) in the theorem 1 and the following assumption, there exists a unique and optimal solution vector for the optimization problem (12)(13)(14).…”

“…Proof: Please refer to [12]. Many iterative methods have been proposed which lead to the optimal solution of constrained optimization problem (12)(13)(14) with the additional assumptions in (17) and (19) or (20) [8].…”

“…Many iterative methods have been proposed which lead to the optimal solution of constrained optimization problem (12)(13)(14) with the additional assumptions in (17) and (19) or (20) [8]. From these methods we have selected the penalty function approach.…”

“…Proof: Please refer to [12]. As it is described in [8], we must solve a projected version of unconstrained optimization (21).…”