A Network-Layer QoE Model for YouTube Live in Wireless Networks

@article{Jimnez2019ANQ,
  title={A Network-Layer QoE Model for YouTube Live in Wireless Networks},
  author={Luis Roberto Jim{\'e}nez and M. Solera and M. Toril},
  journal={IEEE Access},
  year={2019},
  volume={7},
  pages={70237-70252}
}
YouTube Live is one of the most popular services on the Internet, enabling easy streaming of a live video with the acceptable video quality. [...] Key Method The inputs to the model are TCP/IP metrics, from which four service key performance indicators (S-KPIs) are estimated: initial video play start time, video interruption duration, video interruption frequency, and image quality.Expand
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References

SHOWING 1-10 OF 40 REFERENCES
QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities
TLDR
Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities. Expand
A system testbed for modeling encrypted video-streaming service performance indicators based on TCP/IP metrics
TLDR
A system testbed is presented for automatically constructing a simple, albeit accurate, Quality-of-Experience (QoE) model for encrypted video-streaming services in a wireless network. Expand
Modeling the YouTube stack: From packets to quality of experience
TLDR
This paper provides an extensive compendium of objective tools and models for network operators to better understand the YouTube traffic in their networks, to predict the playback behavior of the video player, and to assess how well they are doing in practice in delivering YouTube videos to their customers. Expand
YOUQMON: a system for on-line monitoring of YouTube QoE in operational 3G networks
TLDR
This paper introduces YOUQMON, a novel on-line monitoring system for assessing the Quality of Experience (QoE) undergone by HSPA/3G customers watching YouTube videos, using network-layer measurements only, and evaluates the stalling detection performance of the system. Expand
Monitoring YouTube QoE: Is Your Mobile Network Delivering the Right Experience to your Customers?
TLDR
This paper presents a complete study on the problem of YouTube Quality of Experience monitoring and assessment in mobile networks and considers not only the QoE analysis, modeling and assessment based on real users' experience, but also the passive monitoring of the quality provided by the ISP to its end-customers in a large mobile broadband network. Expand
Internet Video Delivery in YouTube: From Traffic Measurements to Quality of Experience
TLDR
This chapter investigates HTTP video streaming over the Internet for the YouTube platform and the impact of delivery via the Internet on the user experienced quality (QoE) of YouTube video streaming is quantified. Expand
A Survey on Quality of Experience of HTTP Adaptive Streaming
TLDR
The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. Expand
Towards QoE-aware video streaming using SDN
TLDR
This proposed SDN application is designed to monitor network conditions of streaming flow in real time and dynamically change routing paths using multi-protocol label switching (MPLS) traffic engineering (TE) to provide reliable video watching experience. Expand
Measuring the quality of experience of HTTP video streaming
TLDR
Analysis of the relationship among three levels of quality of service (QoS) of HTTP video streaming reveals that the frequency of rebuffering is the main factor responsible for the variations in the QoE. Expand
QoE assessment model for video streaming service using QoS parameters in wired-wireless network
TLDR
Network and service providers can predict perceptual quality and quality satisfaction using the proposed model and provide optimal service environment through selection of video streaming data rate and network traffic control based on video QoE assessment model. Expand
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