Video stream quality impacts viewer behavior: inferring causality using quasi-experimental designs

By Shankar Krishnan, Ramesh K. Sitaraman. ACM/SIGCOMM Internet Measurement Conference, 2012

573 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a58b4f1a1052130c6086fc83c1b409001373f1c7

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Citations in the corpus

Oboe: auto-tuning video ABR algorithms to network conditions (2018, background): “Several researchers have pointed out that sub-optimal ABR performance can significantly impact user-engagement and hence revenue [16, 37].”
A buffer-based approach to rate adaptation (2014, background): “User engagement and quality of experience (QoE) are known to depend on rebuﬀering rate and video rate [7, 11, 14], as well as the delay before playing and how often the video rate changes [7, 19].”
CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction (2016, background): “Even though the above discussion and prior work make the case for throughput prediction, there has been little work on understanding how predictable throughput is in the wild or what types of prediction algorithms we need to use in the context of video bitrate adaptation.”
A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP (2015, methodology): “…→Network protocol design; Application layer protocols; Keywords Internet Video; Bitrate Adaptation; DASH; Model Predictive Control Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or…”
Developing a predictive model of quality of experience for internet video (2013, methodology): “The data used in this paper was collected by conviva.com in real time using a client-side instrumentation library.”
Understanding the impact of network dynamics on mobile video user engagement (2014, background): “Krishnan and Sitaraman also conducted a large scale, passive, user-side study to understand the impact of video streaming quality on user engagement [15].”
Learning in situ: a randomized experiment in video streaming (2019, background): “The importance tradeoff for these factors is captured in a QoE metric; several studies have calibrated QoE metrics against human behavior or opinion [6, 12, 21].”
From QoS to QoE: A Tutorial on Video Quality Assessment (2015, methodology): “…Causality Analysis: To verify the existence of causal relationship between QoS and QoE, a QED-based model is built to identify the QoS metrics that have a signiﬁcant causal effect on the QoE metrics, thus providing a guidance to service providers of which QoS metrics should be optimized [152].”
Neural Adaptive Video Streaming with Pensieve (2017, background): “Many studies have shown that users will quickly abandon video sessions if the quality is not sufﬁcient, leading to signiﬁcant losses in revenue for content providers [12, 25].”