A survey on parametric QoE estimation for popular services. (1st January 2017)

• Record Type: Journal Article
• Authors: Tsolkas, Dimitris; Liotou, Eirini; Passas, Nikos; Merakos, Lazaros
• Is Part Of: Journal of network and computer applications. Volume 77(2017)
• Page Start: 1
• Page End: 17
• Publication Date: 2017-01-01
• Subjects: Quality of experience -- Objective quality estimation -- Parametric models -- Key performance indicators -- E-model; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2016.10.016
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: As we are moving forward to the 5G era, we are witnessing a transformation in the way networks are designed and behave, with the end-user placed at the epicenter of any decision. One of the most promising contributors towards this direction is the shift from Quality of Service (QoS) to Quality of Experience (QoE) service provisioning paradigms. QoE, i.e., the degree of delight or annoyance of a service as this is perceived by the end-user, paves the way for flexible service management and personalized quality monitoring. This is enabled by exploiting parametric QoE assessment models, namely specific formula-based QoE estimation methods. In this paper, recognizing a gap in the literature between the lack of a proper manual regarding the objective QoE estimation and the ever increasing interest from network stakeholders for QoE intelligence, we provide a comprehensive guide to standardized and state-of-the-art quality assessment models. More specifically, we identify and describe parametric QoE formulas for the most popular service types (i.e., VoIP, online video, video streaming, web browsing, Skype, IPTV and file download services), indicating the key performance indicators (KPIs) and major configuration parameters (MCPs) per type. Throughout the paper, it is revealed that KPIs and MCPs are highly variant per service type, and that, even for the same service, different factors contribute with a different weight on the perceived QoE. This finding can strongly enable a more meaningful resource provisioning across different applications compared to QoE-agnostic schemes. Overall, this paper is a stand-alone, self-contained repository of QoE assessment models for the most common applications, becoming a handy tutorial to parties interested in delving more into QoE network management topics. Highlights: Parametric QoE estimation is essential for live network monitoring and management. The key performance indicators that affect QoE highly differ per service type. Awareness of KPIs enables a smarter cross-service resource provisioning.