In this paper, we empirically demonstrate the growing importance of reliability by measuring its effect on user behavior. We present an approach for broadband reliability characterization using data collected by many emerging national initiatives to study broadband and apply it to the data gathered by the Federal Communications Commission's Measuring Broadband America project. Motivated by our findings, we present the design, implementation, and evaluation of a practical approach for improving the reliability of broadband Internet access with multihoming.
Recent work has explored the effect of network factors on user experience with applications, including VoIP @cite_17 , Web @cite_0 and Internet video @cite_43 . Rather than deriving a model for user experience based on multiple factors, our work focuses on the effect of reliability on user demand. Others have started to explore the use of alternative experimental designs to evaluate user experience. @cite_36 apply quasi-experimental design to evaluate the effect of video stream quality on viewer behavior, @cite_23 relies on it for causal analysis of user behavior in social media. @cite_65 explores the effect of contextual factors such as price and competition on user demand. We apply similar methods to understand the effect of service reliability on user behavior.
@cite_36 @cite_65 @cite_0 @cite_43 @cite_23 @cite_17
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Mobile network operators have a significant interest in the performance of streaming video on their networks because network dynamics directly influence the Quality of Experience (QoE). However, unlike video service providers, network operators are not privy to the client- or server-side logs typically used to measure key video performance metrics, such as user engagement. To address this limitation, this paper presents the first large-scale study characterizing the impact of cellular network performance on mobile video user engagement from the perspective of a network operator. Our study on a month-long anonymized data set from a major cellular network makes two main contributions. First, we quantify the effect that 31 different network factors have on user behavior in mobile video. Our results provide network operators direct guidance on how to improve user engagement --- for example, improving mean signal-to-interference ratio by 1 dB reduces the likelihood of video abandonment by 2 . Second, we model the complex relationships between these factors and video abandonment, enabling operators to monitor mobile video user engagement in real-time. Our model can predict whether a user completely downloads a video with more than 87 accuracy by observing only the initial 10 seconds of video streaming sessions. Moreover, our model achieves significantly better accuracy than prior models that require client- or server-side logs, yet we only use standard radio network statistics and or TCP IP headers available to network operators. Quality of experience (QoE) has significant influence on whether or not a user will choose a service or product in the competitive era. For multimedia services, there are various factors in a communication ecosystem working together on users, which stimulate their different senses inducing multidimensional perceptions of the services, and inevitably increase the difficulty in measurement and estimation of the user's QoE. In this paper, a user-centric objective QoE evaluation model (QAVIC model for short) is proposed to estimate the user's overall QoE for audiovisual services, which takes account of perceptual audiovisual quality (QAV) and user interest in audiovisual content (IC) amongst influencing factors on QoE such as technology, content, context, and user in the communication ecosystem. To predict the user interest, a number of general viewing behaviors are considered to formulate the IC evaluation model. Subjective tests have been conducted for training and validation of the QAVIC model. The experimental results show that the proposed QAVIC model can estimate the user's QoE reasonably accurately using a 5-point scale absolute category rating scheme. We present the first study of broadband services in their broader context, evaluating the impact of service characteristics (such as capacity, latency and loss), their broadband pricing and user demand. We explore these relationships, beyond correlation, with the application of natural experiments. Most efforts on broadband service characterization have so far focused on performance and availability, yet we lack a clear understanding of how such services are being utilized and how their use is impacted by the particulars of the market. By analyzing over 23-months of data collected from 53,000 end hosts and residential gateways in 160 countries, along with a global survey of retail broadband plans, we empirically study the relationship between broadband service characteristics, pricing and demand. We show a strong correlation between capacity and demand, even though subscribers rarely fully utilize their links, but note a law of diminishing returns with relatively smaller increases in demand at higher capacities. Despite the fourfold increase in global IP traffic, we find that user demand on the network over a three year period remained constant for a given bandwidth capacity. We exploit natural experiments to examine the causality between these factors. The reported findings represent an important step towards understanding how user behavior, and the market features that shape it, affect broadband networks and the Internet at large. In this paper, we examine mobile users' behavior and their corresponding video viewing patterns from logs extracted from the servers of a large scale VoD system. We focus on the analysis of the main discrepancies that might exist when users access the VoD system catalog from WiFi or 3G connections. We also study factors that might impact mobile users' interests and video popularity. The users' behavior exhibits strong daily and weekly patterns, with mobile users' interests being surprisingly spread across almost all categories and video lengths, independently of the connection type. However, by examining the activity of users individually, we observed a concentration of interests and peculiar access patterns, which allows to classify the users and thus better predict their behavior. We also find a skewed video popularity distribution and then demonstrate that the popularity of a video can be predicted using its very early popularity level. We further analyzed the sources of video viewing and found that even if search engines are the dominant sources for a majority of videos, they represent less than 10 (resp. 20 ) of the sources for the highly popular videos in 3G (resp. WiFi) network. We report that both the type of connections and mobile devices in use have an impact on the viewing time and the source of viewing. Based on our findings, we provide insights and recommendations that can be used to design intelligent mobile VoD systems and help improving personalized services on these platforms.