No 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.

No abstract

Video quality measurement is an important component in the end-to-end video delivery chain. Video quality is, however, subjective, and thus, there will always be interobserver differences in the subjective opinion about the visual quality of the same video. Despite this, most existing works on objective quality measurement typically focus only on predicting a single score and evaluate their prediction accuracies based on how close it is to the mean opinion scores (or similar average based ratings). Clearly, such an approach ignores the underlying diversities in the subjective scoring process and, as a result, does not allow further analysis on how reliable the objective prediction is in terms of subjective variability. Consequently, the aim of this paper is to analyze this issue and present a machine-learning based solution to address it. We demonstrate the utility of our ideas by considering the practical scenario of video broadcast transmissions with focus on digital terrestrial television (DTT) and proposing a no-reference objective video quality estimator for such application. We conducted meaningful verification studies on different video content (including video clips recorded from real DTT broadcast transmissions) in order to verify the performance of the proposed solution.Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 01/02/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Accuracy (in general) Higher than RR and NR. Higher than NR; lower than FR. Lower than FR and RR.

While video streaming has dominated the Internet traffic, Video Service Providers (VSPs) compete on how to assure the best Quality of Experience (QoE) to their customers. HTTP Adaptive Streaming (HAS) has become the de facto way that helps VSPs work-around potential network bottlenecks that inevitably cause stallings. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the Mobile Network Operators' (MNOs) infrastructure and evolving network conditions. Software-Defined Networking (SDN) has brought new perspectives to this traditional paradigm where VSPs and MNOs are isolated, allowing the latter to open their network for more flexible, service-oriented programmability. This paper takes advantage of recent standardization trends in SDN and proposes a programmable QoE-SDN APP, enabling network exposure feedback from MNOs to VSPs towards network-aware video segment selection and caching, in the context of HAS. The video selection problem is formulated using Knapsack optimization and relaxed to partial sub-problems that provide segment encodings that can mitigate stallings. Furthermore, a mobility prediction mechanism based on the SLAW model is introduced, towards proactive segment caching.

This chapter discusses prospects of QoE management for future networks and applications. After motivating QoE management, it first provides an introduction to the concept by discussing its origins, key terms and giving an overview of the most relevant existing theoretical frameworks. Then, recent research on promising technical approaches to QoE-driven management that operate across different layers of the networking stack is discussed. Finally, the chapter provides conclusions and an outlook on the future of QoE management with a focus on those key enablers (including cooperation, business models and key technologies) that are essential for ultimately turning QoE-aware network and application management into reality.

No abstract

This paper provides a brief overview and a vision for introducing a Quality of Experience (QoE) function for on-demand services or for premium users, based-on Software-Defined Networking (SDN). The proposed "QoE-service" can take advantage of the SDN global resource view and complementary QoE metrics to assure the desired performance for OTT applications by adopting traffic management mechanisms. This paper introduces the QoE-Service concept and SDN architecture and it presents a set of use cases that demonstrate its suitability and applicability to Long Term Evolution (LTE) networks.