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Title: Video QoE Estimation and Diagnosis using Network Measurement Data
Tarun Mangla
Ph.D. Student in Computer Science
School of Computer Science
College of Computing
Georgia Institute of Technology
Date: Thursday, November 21, 2019
Time: 2:00 pm - 4:00 pm (EST)
Location: Klaus 1202
Committee:
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Dr. Mostafa H. Ammar (Co-advisor), School of Computer Science, Georgia Institute of Technology
Dr. Ellen W. Zegura (Co-advisor), School of Computer Science, Georgia Institute of Technology
Dr. Constantine Dovrolis, School of Computer Science, Georgia Institute of Technology
Dr. Ada Gavrilovska, School of Computer Science, Georgia Institute of Technology
Dr. Emir Halepovic, AT&T Labs Research
Abstract
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More than even before, last-mile Internet Service Providers (ISPs) are forced to efficiently provision and manage their networks to meet the growing demand for Internet video (expected to be 82% of the global IP traffic in 2022). This network optimization requires ISPs to have an in-depth understanding of end-user video Quality of Experience (QoE) and the factors leading to QoE degradation. Understanding video QoE, let alone troubleshooting, is challenging for ISPs as they generally do not have access to applications at end-user devices to observe key objective metrics impacting QoE. Instead, they have to rely on measurement of network traffic to estimate objective QoE metrics and use it for troubleshooting QoE issues. However, this can be challenging for HTTP-based Adaptive Streaming (HAS) video, the de facto standard for streaming over the Internet, because of the complex relationship between the network observable data and the video QoE metrics. This mainly results from its robustness to short-term variations in the underlying network conditions due to the use of the video buffer and bitrate adaptation. In this proposal, we provide methods for video QoE estimation and troubleshooting using network measurement data; thus, facilitating a video QoE-aware network management.
We develop methods for QoE estimation that model video sessions based on the network traffic dynamics of the HAS protocol; thus, making them fairly generalizable and minimally dependent on ground truth QoE metrics. We first develop MIMIC that estimates unencrypted video QoE using HTTP logs. We do a large-scale validation of MIMIC using ground truth QoE metrics from a popular video streaming service. We also deploy MIMIC in a real-world cellular network and demonstrate some preliminary use cases of QoE estimation for ISPs. We then develop eMIMIC to estimate QoE metrics for encrypted video using packet-level traces. We evaluate eMIMIC using an automated experimental framework under realistic network conditions and show that it outperforms state-of-the-art QoE estimation approaches. Finally, we focus on the problem of troubleshooting video QoE issues. We propose to design methods that can detect, localize, and identify video QoE issues in the network using the following two-phased approach: i) clustering video QoE estimates based on network path and streaming context for localization of issues, and ii) augmenting passive measurements with the network health data for diagnosing issues within the ISP network.
