Quality of experience-oriented cross-layer downlink scheduling for heterogeneous traffic in long term evolution networks

• Main Author: Nasimi, Meysam
• Format: Thesis
• Language: English
• Published: 2014
• Online Access: http://psasir.upm.edu.my/id/eprint/48048/1/FK%202014%2036R.pdf; http://psasir.upm.edu.my/id/eprint/48048/

Long Term Evolution (LTE) is a recently evolving technology proposed by Third Generation Partnership Project (3GPP) to provide a smooth migration towards Fourth Generation (4G) of cellular networks. Due to the ever-increasing demands for high speed data communication along with transmission of various types of services over cellular networks, it is of vital importance for the LTE system to has an efficient radio resource management. In particular, satisfying the Quality of Service (QoS) requirements of different applications is one of the key challenges of radio resource management that needs to be dealt by the LTE system. In this thesis, a cross-layer scheduler that interact between three different layers of wireless protocol stack, namely application, the Medium Access Control (MAC) and physical layer is proposed. The cross-layer scheduler provides efficient allocation of the wireless resources across different types of application (i.e., real-time and non real-time) run by different users to maximize network resource utilization and user-perceived quality of service, or also known as Quality of Experience (QoE). Here, Mean Opinion Score (MOS) is used as a unified QoE metric that indicates the user-perceived quality for real-time or multimedia services, notably video applications. Along with multimedia services, the proposed framework also takes care of non-real-time traffic by ensuring a certain level of fairness. In the proposed framework, different modules were employed to handle cross-layer scheduling, including video application, Cross Layer Resource Allocator (CLRA), scheduler and transmitter. Video application module at the application layer buffers the incoming video from backbone and reports video distortion to CLRA module. Next, CLRA exploits the video distortion along with channel distortion from physical layer to estimate MOS value. Finally, based on the obtained MOS value, frame priority weight, QoS delay constraints and channel quality status,in every TTI, the user with the highest weight metric will obtain scheduling opportunity. To appreciate the effectiveness of the proposed framework, two different scenarios of single-cell and multi-cell were taken into considerations. The simulation, applied to scenarios where users simultaneously run different types of applications, confirms that the proposed QoE-oriented cross-layer framework leads to 14.2% and 18% of improvement in terms of user-perceived quality and spectral efficiency respectively.