Quality of Experience in Mobile Peer-to-Peer Streaming Systems

Quality of Experience in Mobile Peer-to-Peer Streaming Systems

Florence Agboma (University of Essex, UK)
Copyright: © 2012 |Pages: 46
DOI: 10.4018/978-1-4666-1613-4.ch009
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This chapter considers the various parameters that affect the user’s Quality-of-Experience (QoE) in mobile peer-to-peer streaming systems, which are a form of content delivery network. Network and content providers do not necessarily focus on users’ QoE when designing the content delivery strategies and business models. The outcome of this is quite often the over-provisioning of network resources and also a lack of knowledge in respect to the user’s satisfaction. The focus is the methodology for quantifying the user’s perception of service quality for mobile video services and user contexts. The statistical technique of discriminant analysis is employed in defining prediction models to map Quality-of-Service (QoS) parameters onto estimates of the user’s QoE ratings. The chapter considers the relative contribution of the QoS parameters to predicting user responses. The chapter also demonstrates the value of the prediction models in developing QoE management strategies in order to optimize network resource utilization. To investigate the versatility of the framework, a feasibility study was applied to a P2P TV system. P2P systems continue to develop and as such, not a lot is known about their QoE characteristics, which situation this chapter seeks to remedy.
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With the growth in the availability of multimedia services, coupled with the technological advances in the mobile access devices, Quality of Service (QoS) has become the adopted set of technologies used in managing network traffic in the delivery systems that provide these services. The aim of QoS has been to manage the performance of networks and to provide availability guarantees to network traffic. QoS enables the measurement of network parameters, and the detection of changing network conditions (such as congestion or availability of bandwidth). This information is utilized in resource management by prioritizing traffic. However, QoS processes by themselves are not adequate enough in that they do not take into account the user’s perception of network performance and service quality. There is now a realization that measuring and studying users’ Quality of Experience (QoE) should be an important metric to be employed during the design and management of content delivery systems and other engineering processes. This is because QoE is a metric that refers to a measure of the end-to-end performance at the service level from the user's perspective. This metric is measured at the end devices and can conceptually be seen as the remaining quality after the distortions introduced during the preparation of the content and the delivery through the network until it reaches the decoder at the end device.

This Chapter presents a QoE-based framework as an invaluable tool in resource management. There are many definitions of QoE (Siller, 2006), (Soldan, et al., 2006), (Patrick, et al., 2004), (Nokia, 2004), (ITU-T Study Group 12, 2007) all of which share a similar concept i.e., QoE relates to the user satisfaction of the offered service. The definition provided by (Soldani et al., 2006) is the favored one in the context of this Chapter: “QoE is the perception of the user about the quality of a particular service or network”. QoE strongly depends on the expectations of the users on the offered service. The provision of all the appropriate QoS conditions does not by itself guarantee a satisfied user. Thus, there is the need to understand users’ QoE in order to use QoS effectively. Put another way, understanding of the user QoE will lead to better resource management strategies, and the discovery of ways of increasing and maintaining user satisfaction

The ability to manage service quality has become an essential part of the service delivery chain, and it is an important differentiator between the service qualities being offered by service and content providers. The types of online services that are likely to emerge include Video-on-Demand (VoD) and live streaming. As a result of this, service and content providers would be very attracted to the prospects of being able to offer user specific charging schemes and service quality in order to satisfy a large and varied user or customer base. The providers would be able to implement such user specific services only if there is a thorough understanding of users’ QoE.

The proliferation of different types of access devices further highlights the importance of QoE research. As an illustration; the QoE for a user watching a news clip on a PDA will most likely differ from another user watching that same news clip on a 3G mobile phone. This is because the two terminals come with different display screens, bandwidth capabilities, frame rates, codecs and processing power. Therefore, delivering multimedia contents or services to these two terminal types without carefully thinking about the users’ quality expectations or requirements for these terminal types, might lead to service over provisioning and network resource wastage. Thus, it is essential to determine the thresholds of quality acceptability across contents and terminal types. With this information, content and network providers will have the capability to minimize storage and network resources by allocating only the resources that are sufficient to maintain a specific level of user satisfaction.

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