Mobile data service QoE analytics and optimization

Quality of Experience (QoE) has emerged as the key performance metric to evaluate end-user service quality perception. We propose a system for video and web QoE assessment in LTE networks that we call QoE Analytics system. We build the proposed system and run it on a LTE network and successfully est...

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Published in:2015 IEEE International Conference on Communication Workshop (ICCW) pp. 1699 - 1704
Main Authors: Soon Young Yoon, Suwon Lee, Youngjin Kim, Panhyung Lee, Chang-Yeong Oh, Iljin Youn, Monroy, Edwin, Hasany, Ziaul, Jungah Choi
Format: Conference Proceeding
Language:English
Published: IEEE 01-06-2015
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Abstract Quality of Experience (QoE) has emerged as the key performance metric to evaluate end-user service quality perception. We propose a system for video and web QoE assessment in LTE networks that we call QoE Analytics system. We build the proposed system and run it on a LTE network and successfully estimates QoE metrics per UE in real-time by inspecting user plane data on S1 interface without any QoE report from UEs. QoE metrics measured by QoE Analytics are verified to be in close match to actual UE observations such as initial playout delay, rebuffering percentage and rebuffering duration for video applications and download time for web traffic. Optimization of eNB scheduler performance based on this real-time QoE Analytics feedback is demonstrated by video QoE-aware scheduler and a significant improvement in QoE metrics is observed in congested situation.
AbstractList Quality of Experience (QoE) has emerged as the key performance metric to evaluate end-user service quality perception. We propose a system for video and web QoE assessment in LTE networks that we call QoE Analytics system. We build the proposed system and run it on a LTE network and successfully estimates QoE metrics per UE in real-time by inspecting user plane data on S1 interface without any QoE report from UEs. QoE metrics measured by QoE Analytics are verified to be in close match to actual UE observations such as initial playout delay, rebuffering percentage and rebuffering duration for video applications and download time for web traffic. Optimization of eNB scheduler performance based on this real-time QoE Analytics feedback is demonstrated by video QoE-aware scheduler and a significant improvement in QoE metrics is observed in congested situation.
Author Jungah Choi
Chang-Yeong Oh
Hasany, Ziaul
Panhyung Lee
Monroy, Edwin
Soon Young Yoon
Suwon Lee
Youngjin Kim
Iljin Youn
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  surname: Jungah Choi
  fullname: Jungah Choi
  organization: Adv. Technol. Group, Samsung Electron., Suwon, South Korea
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Snippet Quality of Experience (QoE) has emerged as the key performance metric to evaluate end-user service quality perception. We propose a system for video and web...
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SubjectTerms Delays
Internet
Mobile communication
Quality of service
Servers
Streaming media
Title Mobile data service QoE analytics and optimization
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