Fair Optimization of Video Streaming Quality of Experience in LTE Networks Using Distributed Antenna Systems and Radio Resource Management

Elias Yaacoub,Zaher Dawy

doi:10.1155/2014/562079

Abstract

Network quality of experience (QoE) metrics are proposed in order to capture the overall performance of radio resource management (RRM) algorithms in terms of video quality perceived by the end users. Metrics corresponding to average, geometric mean, and minimum QoE in the network are measured when Max C/I, proportional fair, and Max-Min RRM algorithms are implemented in the network. The objective is to ensure a fair QoE for all users in the network. In our study, we investigate both the uplink (UL) and downlink (DL) directions, and we consider the use of distributed antenna systems (DASs) to enhance the performance. The performance of the various RRM methods in terms of the proposed network QoE metrics is studied in scenarios with and without DAS deployments. Results show that a combination of DAS and fair RRM algorithms can lead to significant and fair QoE enhancements for all the users in the network.

Highlights

With the increased video traffic in state-of-the art cellular networks, it is imperative to enhance the quality of service (QoS) of video transmissions, usually represented by the video peak signal to noise ratio (PSNR)
Network quality of experience (QoE) metrics are proposed in order to capture the overall performance of radio resource management (RRM) algorithms in terms of video quality perceived by the end users
Geometric mean, and minimum QoE in the network are measured when Max C/I, proportional fair, and Max-Min RRM algorithms are implemented in the network

Summary

Introduction

With the increased video traffic in state-of-the art cellular networks, it is imperative to enhance the quality of service (QoS) of video transmissions, usually represented by the video peak signal to noise ratio (PSNR). Video quality of experience (QoE) is gaining significant interest as a method to quantify the multimedia experience of mobile users; for example, see [1]. It can be considered as a “perceived QoS,” and reflects better than QoS the quality of the video as seen by the mobile users. QoE measures are based on subjective assessment of video quality by the users. The novelty in this work is in proposing metrics for assessing the QoE performance over the whole network, taking into account fairness constraints in the QoE perceived by different users. We study the impact of different radio resource management (RRM) algorithms on optimizing the network QoE performance and ensuring fairness towards the various users in the network

Objectives

Results

Conclusion