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 |
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Main Authors: | , , , , , , , , |
Format: | Conference Proceeding |
Language: | English |
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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. |
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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 |
Author_xml | – sequence: 1 surname: Soon Young Yoon fullname: Soon Young Yoon organization: Adv. Technol. Group, Samsung Electron., Suwon, South Korea – sequence: 2 surname: Suwon Lee fullname: Suwon Lee organization: Adv. Technol. Group, Samsung Electron., Suwon, South Korea – sequence: 3 surname: Youngjin Kim fullname: Youngjin Kim organization: Adv. Technol. Group, Samsung Electron., Suwon, South Korea – sequence: 4 surname: Panhyung Lee fullname: Panhyung Lee organization: Adv. Technol. Group, Samsung Electron., Suwon, South Korea – sequence: 5 surname: Chang-Yeong Oh fullname: Chang-Yeong Oh organization: Adv. Technol. Group, Samsung Electron., Suwon, South Korea – sequence: 6 surname: Iljin Youn fullname: Iljin Youn organization: Adv. Technol. Group, Samsung Electron., Suwon, South Korea – sequence: 7 givenname: Edwin surname: Monroy fullname: Monroy, Edwin organization: Adv. Technol. Group, Samsung Electron., Suwon, South Korea – sequence: 8 givenname: Ziaul surname: Hasany fullname: Hasany, Ziaul email: ziaul.hashmi@samsung.com organization: Adv. Technol. Group, Samsung Electron., Suwon, South Korea – sequence: 9 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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Title | Mobile data service QoE analytics and optimization |
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