Name
Abstract | ||
|---|---|---|
Video applications such as video-on-demand and videoconferencing over wireless networks require system resources to be allocated dynamically and optimally in accordance with the time-varying environment and video contents. User-perceived quality is tending to be a crucial factor in evaluating the success of video applications. The aim of this paper is to propose a cross-layer design scheme for optimizing resource allocation of video applications over Long Term Evolution (LTE) networks based on Quality of Experience (QoE) evaluation. We propose a novel mapping model between Peak Signal-to-Noise Ratio (PSNR) and Mean Opinion Score (MOS) based on a hyperbolic tangent function, which can reflect the relation between objective system parameters and subjective perceived quality simply and precisely. The cross-layer architecture presented in this paper jointly optimizes the Application (APP) layer, the Media Access Control (MAC) layer and the Physical (PHY) layer of the wireless protocol stack. On the basis of this architecture, we present a QoE prediction function and utilize the Particle Swarm Optimization (PSO) method to solve the resource allocation problem. Simulation results show that the proposed scheme significantly outperforms the traditional scheduling scheme in terms of maximizing user-perceived quality as well as maintaining fairness among users. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s11042-013-1413-0 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Wireless video applications,Cross-layer,QoE,PSO,LTE | Wireless network,Media access control,Computer science,Computer network,Real-time computing,Quality of experience,Artificial intelligence,Wireless Application Protocol,Videoconferencing,Computer vision,Mean opinion score,Resource allocation,PHY | Journal |
Volume | Issue | ISSN |
72 | 2 | 1380-7501 |
Citations | PageRank | References |
6 | 0.43 | 35 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| ying ai ju | 1 | 56 | 5.05 |
| Zhaoming Lu | 2 | 168 | 53.12 |
| Dabing Ling | 3 | 36 | 3.10 |
| Xiangming Wen | 4 | 618 | 82.20 |
| Wei Zheng | 5 | 342 | 30.46 |
| Wenmin Ma | 6 | 102 | 5.33 |