Q-score: Proactive Service Quality Assessment in a Large IPTV System

Abstract — In large-scale IPTV systems, it is essential to main-tain high service quality while providing a wider variety of service features than typical traditional TV. Thus service quality assess-ment systems are of paramount importance as they monitor the user-perceived service quality and alert when issues occurs. For IPTV systems, however, there is no simple metric to represent user-perceived service quality and Quality of Experience (QoE). More-over, there is only limited user feedback, often in the form of noisy and delayed customer calls. Therefore, we aim to approximate the QoE through a selected set of performance indicators in a proac-tive (i.e., detect issues before customers reports to call centers) and scalable fashion. In this paper, we present a service quality assessment framework, Q-score, which accurately learns a small set of performance indica-tors most relevant to user-perceived service quality, and proactively infers service quality in a single score. We evaluate Q-score using network data collected from a commercial IPTV service provider and show that Q-score is able to predict 60 % of the service prob-lems that are reported by customers with 0.1 % false positives. Through Q-score, we have (i) gained insight into various types of service problems causing user dissatisfaction, including why users tend to react promptly to sound issues while late to video issues; (ii) identified and quantified the opportunity to proactively detect the service quality degradation of individual customers before se-vere performance impact occurs; and (iii) observed possibility to allocate customer care workforce to potentially troubling service areas before issues break out