A Mobile Fog Computing-Assisted DASH QoE Prediction Scheme. (28th August 2018)

• Record Type: Journal Article
• Authors: Zheng, Hongyun; Zhao, Yongxiang; Lu, Xi; Cao, Rongzhen
• Other Names: Gong Xiaowen Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2018(2018)
• Publication Date: 2018-08-28
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2018/6283957
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Video service has become a killer application for mobile terminals. For providing such services, most of the traffic is carried by the Dynamic Adaptive Streaming over HTTP (DASH) technique. The key to improve video quality perceived by users, i . e., Quality of Experience (QoE), is to effectively characterize it by using measured data. There have been many literatures that studied this issue. Some existing solutions use probe mechanism at client/server, which, however, are not applicable to network operator. Some other solutions, which aimed to predict QoE by deep packet parsing, cannot work properly as more and more video traffic is encrypted. In this paper, we propose a fog-assisted real-time QoE prediction scheme, which can predict the QoE of DASH-supported video streaming using fog nodes. Neither client/server participations nor deep packet parsing at network equipment is needed, which makes this scheme easy to deploy. Experimental results show that this scheme can accurately detect QoE with high accuracy even when the video traffic is encrypted.