<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div class=""><b class=""><span style="font-size: 14px;" class="">Tarun Mangla</span></b></div><div class=""><b style="font-size: 14px;" class="">PhD Student, Georgia Institute of Technology</b></div><div class=""><b style="font-size: 14px;" class="">Host: Nick Feamster</b></div><div class=""><b class=""><br class=""></b></div><div class=""><b style="font-size: 14px;" class="">Title: “Video QoE Estimation Using Network Measurement Data"</b></div><div class=""><br class=""></div><div class=""><b style="font-size: 14px;" class="">Tuesday January 21st</b></div><div class=""><b style="font-size: 14px;" class="">3:00-4:00pm, JCL 298</b></div><div class=""><b style="font-size: 14px;" class="">Refreshments will be served.</b></div><div class=""><b class=""><span lang="EN-GB" class=""><br class=""></span></b></div><div class=""><font size="2" class=""><b class=""><span lang="EN-GB" class="">Abstract</span></b><span lang="EN-GB" class="">: More than ever before,
last-mile Internet Service Providers (ISPs) are forced to efficiently provision
and manage their networks to meet the growing demand for Internet video. This network
optimization requires ISPs to have an in-depth understanding of end-user video
Quality of Experience (QoE). However, understanding video QoE is challenging
for ISPs as they generally do not have access to applications at end-user
devices. Instead, they rely on measurements of network traffic to estimate
objective QoE metrics. This is challenging due to the complex relationship
between the network observable data and the video QoE metrics resulting from
HTTP-based Adaptive Streaming (HAS) mechanisms; these mechanisms provide
robustness to short-term variations in the underlying network conditions by
using a video buffer and bitrate adaptation.</span></font></div><div class=""><font size="2" class=""><br class=""></font></div><div class=""><font size="2" class="">In this talk, I will present methods
that enable ISPs to infer video QoE from passive network measurements. Our
developed methods model a video session based on the network traffic dynamics
of the HAS protocol; thus, making them fairly generalizable and minimally
dependent on ground truth QoE metrics. I will first describe MIMIC, a session
modeling-based approach to infer QoE for unencrypted video. I will then
describe how we extend MIMIC to infer QoE for encrypted video using packet
traces. Finally, I will describe VideoNOC, a video QoE inference system for
cellular ISPs. In designing VideoNOC, we address system challenges to measure
video QoE inside a cellular ISP, arising largely because of the scale of the
network traffic and the complex cellular network architecture. VideoNOC is
currently deployed inside a major cellular operator in the US.</font></div><div class=""><b class=""><span lang="EN-GB" class=""><font size="2" class=""><br class=""></font></span></b></div><div class=""><font size="2" class=""><b class=""><span lang="EN-GB" class="">Bio</span></b><span lang="EN-GB" class="">: Tarun Mangla is a PhD student in
the School of Computer Science at the Georgia Institute of Technology,
co-advised by Mostafa Ammar and Ellen Zegura. His research interests span video
streaming, network measurements, and cellular networks. He completed his
bachelors in Computer Science and Engineering from Indian Institute of
Technology, Delhi (2014) and MS in Computer Science from Georgia Tech (2018).
He is a recipient of the Best Paper Award at IFIP TMA, 2018.</span></font></div><div class=""><div class="">
<div dir="auto" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div>—<br class="">Katherine Rosengarten<br class="">Administrative Specialist, Center for Data and Computing (CDAC)<br class="">University of Chicago<br class="">John Crerar Library Building, JCL 262<br class="">773-834-5397</div></div>
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