Interference-Aware Cross-Layer Design for Distributed Video Transmission in Wireless Networks

Abstract

Cross-layer communication could bring significant performance improvement for wireless communication systems through allowing information exchanges among multiple layers. For delay-sensitive video application, cross-layer optimization could be more critical by jointly considering network resource allocation and application performance requirement, especially in an interference-limited distributed network. To improve the quality of video transmission over an ad-hoc network, this paper proposes a cross-layer scheme to maximize the average received video quality by jointly considering the application performance and network transmission strategy. In this scheme, important system parameters including the distribution of the node and the resulting interference, link transmission policy, queueing delay as well as the video encoding rate are jointly considered to achieve the best received video quality. To this end, a thresholdbased transmission strategy and an interference approximation model are firstly developed. Then, the queueing delay is analyzed based on the M/M/1 queue model. Finally, the problem of maximizing video transmission quality is formulated as a crosslayer optimization problem. To solve this optimization problem, a distributed algorithm is proposed based on the optimization theory and game theory. The convergence performance of this distributed algorithm is analyzed and shown with relatively lower complexity through simulation. Furthermore, both numerical and simulation results show that the proposed scheme improves the system performance considerably based on the comparison with other two existing schemes.

Energy‐efficient resource allocation in relay‐aided orthogonal frequency division multiplexing cognitive radio networks with quality of service provisioning

“…The convergence of this algorithm can be guaranteed if the optimization problem (16) is solved properly in each iteration. 23 It should be noted that when we set q = 0 in the optimization problem (16), the optimization problem (16) degenerates to the SE maximization problem which aims at maximizing the CR system's transmission rate, and this problem is similar to the resource allocation problem proposed in Shaat et al 13 Correspondingly, the SE maximization scheme can be obtained by solving the optimization problem (16) with q = 0 and the SE maximization scheme is limited by the same constraints as the EE maximization scheme.…”

“…Owing to the explosive development of high-data-rate services and time-sensitive applications, the energy consumption increases rapidly, which causes a large amount of greenhouse gas and high operating cost for wireless communication systems. 15,16 Therefore, optimizing energy efficiency (EE) defined as the number of bits transmitted per unit of energy is becoming more and more crucial. 17 In general, the SE maximization problem only focus on improving system capacity, while the objective of the EE maximization problem is to improve system capacity and reduce power consumption concurrently.…”

Proactive Jamming Toward Interference Alignment Networks: Beneficial and Adversarial Aspects

“…) U [k] . (5) From (5), we can observe that the transmission rate of IA users will be degraded severely if the jamming signal is not properly removed. The transmission rate will decrease when the transmit power of the jammer increases.…”

Socially aware D2D cooperative communications for enhancing Internet of Things application

“…The noise of the received signal is composed of the additive Gaussian white noise N 0 [30], and the same frequency interference noise by cellular user u. Therefore, the noise of the received signal by user u i can be denoted by P u h u,u j + N 0 .…”