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Recommendations for Big Data in Online Video Quality of Experience Assessment

Recommendations for Big Data in Online Video Quality of Experience Assessment
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摘要 Real-time video application usage is increasing rapidly. Hence, accurate and efficient assessment of video Quality of Experience (QoE) is a crucial concern for end-users and communication service providers. After considering the relevant literature on QoS, QoE and characteristics of video trans-missions, this paper investigates the role of big data in video QoE assessment. The impact of QoS parameters on video QoE are established based on test-bed experiments. Essentially big data is employed as a method to establish a sensible mapping between network QoS parameters and the resulting video QoE. Ultimately, based on the outcome of experiments, recommendations/re- quirements are made for a Big Data-driven QoE model. Real-time video application usage is increasing rapidly. Hence, accurate and efficient assessment of video Quality of Experience (QoE) is a crucial concern for end-users and communication service providers. After considering the relevant literature on QoS, QoE and characteristics of video trans-missions, this paper investigates the role of big data in video QoE assessment. The impact of QoS parameters on video QoE are established based on test-bed experiments. Essentially big data is employed as a method to establish a sensible mapping between network QoS parameters and the resulting video QoE. Ultimately, based on the outcome of experiments, recommendations/re- quirements are made for a Big Data-driven QoE model.
作者 Ethan Court Kapilan Radhakrishnan Kemi Ademoye Stephen Hole Ethan Court;Kapilan Radhakrishnan;Kemi Ademoye;Stephen Hole(School of Applied Computing (SOAC), University of Wales Trinity Saint David, Swansea, UK)
出处 《Journal of Computer and Communications》 2016年第5期24-31,共8页 电脑和通信(英文)
关键词 Quality of Experience QOE Big Data ONLINE VIDEO TRAFFIC Quality of Experience QoE Big Data Online Video Traffic
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