Objective assessment of region of interest-aware adaptive multimedia streaming quality

Adaptive multimedia streaming relies on controlled adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality

QoE-oriented cross-layer downlink scheduling for heterogeneous traffics in LTE networks

With the soaring demands for high speed data communication, as well as transmission of various types of services with different requirements over cellular networks, having a decent radio resource management is considered vital in Long Term Evolution (LTE) system. 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 paper, we propose a cross-layer design scheme that jointly optimizes three different layers of wireless protocol stack, namely application, Medium Access Control (MAC), and physical layer. The cross-layer optimization framework provides efficient allocation of wireless resources across different types of applications (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 certain level of fairness. Our simulation, applied to scenarios where users simultaneously run different types of applications, confirms that the proposed QoE-oriented cross-layer framework leads to significant improvement in terms of maximizing user-perceived quality as well as maintaining fairness among users

Automatic best wireless network selection based on key performance indicators

Introducing cognitive mechanisms at the application layer may lead to the possibility of an automatic selection of the wireless network that can guarantee best perceived experience by the final user. This chapter investigates this approach based on the concept of Quality of Experience (QoE), by introducing the use of application layer parameters, namely Key Performance Indicators (KPIs). KPIs are defined for different traffic types based on experimental data. A model for an ap- plication layer cognitive engine is presented, whose goal is to identify and select, based on KPIs, the best wireless network among available ones. An experimenta- tion for the VoIP case, that foresees the use of the One-way end-to-end delay (OED) and the Mean Opinion Score (MOS) as KPIs is presented. This first implementation of the cognitive engine selects the network that, in that specific instant, offers the best QoE based on real captured data. To our knowledge, this is the first example of a cognitive engine that achieves best QoE in a context of heterogeneous wireless networks