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  1. Proceedings of the 2016 workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE '16)
  2. QoE Inference Without Application Control
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An Educated Guess on QoE in Operational Networks through Large-Scale Measurements
Impact of Access Speed on Adaptive Video Streaming Quality: A Passive Perspective
Predicting the effect of home Wi-Fi quality on Web QoE
QoE Inference Without Application Control
QoE Analysis of DASH Cross-Layer Dependencies by Extensive Network Emulation
Towards a causal analysis of Video QoE from Network and Application QoS
Measuring the Quality of Experience of Web users
BingeOn Under the Microscope: Understanding T-Mobiles Zero-Rating Implementation
Concept for Client-initiated Selection of Cloud Instances for Improving QoE of Distributed Cloud Services
Client-Driven Network-level QoE fairness for Encrypted 'DASH-S'

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QoE Inference Without Application Control

Content Provider ACM Digital Library
Author Madhyastha, Harsha V. Mao, Z. Morley Chen, Qi Alfred Hong, David Ke Nikravesh, Ashkan
Abstract Network quality-of-service (QoS) does not always directly translate to users' quality-of-experience (QoE), e.g., changes in a video streaming app's frame rate in reaction to changes in packet loss rate depend on various factors such as the adaptation strategy used by the app and the app's use of forward error correction (FEC) codes. Therefore, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system (OS). However, today, entities such as ISPs and OSes that implement these optimizations typically do not have a convenient way of obtaining input from applications on user QoE. To address this problem, we propose offline generation of per-application models mapping application-independent QoS metrics to corresponding application-specific QoE metrics, thereby enabling entities (such as ISPs and OSes) that can observe a user's network traffic to infer the user's QoE, in the absence of direct input. In this paper, we describe how such models can be generated and present our results from two popular video applications with significantly different QoE metrics. We also showcase the use of these models for ISPs to perform QoE-aware traffic management and for the OS to offer an efficient QoE diagnosis service.
Ending Page 24
Page Count 6
Starting Page 19
File Format PDF
ISBN 9781450344258
DOI 10.1145/2940136.2940145
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-08-22
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Measurement Qoe Traffic management Video Diagnosis
Content Type Text
Resource Type Article
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