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Nov 14, 2016 · ABSTRACT. Tracking and maintaining satisfactory QoE for video streaming services is becoming a greater challenge for mobile network operators ...
We propose a novel methodology for detecting video streaming QoE issues from encrypted traffic. We develop predictive models for detecting different levels of ...
In this paper we present a framework that is able to extract key QoE metrics such as i) stall detection, ii) average representation (resolution), and iii) ...
Tracking and maintaining satisfactory QoE for video streaming services is becoming a greater challenge for mobile network operators than ever before.
In this paper, we propose DeepQoE, a new approach that enables real-time video QoE measurement from encrypted traffic. We summarize critical fine-grained QoE ...
In this paper, we present a methodology called. eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based ...
In this paper, we propose DeepQoE, a new approach that enables real-time video QoE measurement from encrypted traf- fic. We summarize critical fine-grained QoE ...
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In this paper, we propose multi-task learning with hierarchy (MTLH) model for inferring video QoE metrics. We use shared layer to extract deep features ...
We propose a methodology for network-side video quality of experience (QoE) measurement and monitoring in mobile networks that works with encrypted traffic.
Tracking and maintaining satisfactory QoE for video streaming services is becoming a greater challenge for mobile network operators than ever before.