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Register a new userModeling QoE of Video Streaming in Wireless Networks with Large-Scale Measurement of User Behavior
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Added by
Yuedong Xu
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as starvation probability and buffering time interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on the real traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the views can be categorized into two classes, short and long views with separated time scales. We then map the measured traffic pattern to bandwidth shared cellular networks and propose an analytical framework to compute the closed-form starvation probability on the basis of ordinary differential equations (ODEs). Our framework can be naturally extended to investigate practical issues including the progressive downloading and the finite video duration. Extensive trace-driven simulations validate the accuracy of our models. Our study reveals that the starvation metrics of the short and long views possess different sensitivities to the scheduling priority at base station. Hence, a better QoE tradeoff between the short and long views has a potential to be leveraged by offering them different scheduling weights. The flow differentiation involves tremendous technical and non-technical challenges because video content is owned by content providers but not the network operators and the viewing time of each session is unknown beforehand. To overcome these difficulties, we propose an online Bayesian approach to infer the viewing time of each incoming flow with the least information from content providers.
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Bandwidth requirements of both wireless and wired clients in access networks continue to increase rapidly, primarily due to the growth of video traffic. Application awareness can be utilized in access networks to optimize quality of experience (QoE) of end clients. In this study, we utilize information at the client-side application (e.g., video resolution) to achieve superior resource allocation that improves user QoE. We emphasize optimizing QoE of the system rather than quality of service (QoS), as user satisfaction directly relies on QoE and optimizing QoS does not necessarily optimize QoE, as shown in this study. We propose application-aware resource-allocation schemes on an Ethernet passive optical network (EPON), which supports wireless (utilizing orthogonal frequency division multiple access) and wired clients running video-conference applications. Numerical results show that the application-aware resource-allocation schemes improve QoE for video-conference applications for wired and wireless clients.
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