Developing a predictive model of quality of experience for internet video

By Athula Balachandran, Vyas Sekar, Aditya Akella, Srinivasan Seshan, Ion Stoica, Hui Zhang. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2013

446 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Urban Studies

View Paper: https://www.semanticscholar.org/paper/d284138789e480dec9373fbb53fa47fa19fa5564

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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].”
Low Latency Geo-distributed Data Analytics (2015, background): “, codec, buffer sizes) to improve performance (re-buffering ratio [21]).”
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].”
Next Generation 5G Wireless Networks: A Comprehensive Survey (2016, methodology): “However, Internet video ecosystem lacks regularized quality measurement techniques [174], [175].”
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP (2019, background): “[45] address these issues and propose a data-driven approach that”
A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP (2015, background): “Our goal is to provide a flexible QoE model rather than a fixed notion of QoE as this is an active area of research [19].”
From QoS to QoE: A Tutorial on Video Quality Assessment (2015, methodology): “Moreover, it is shown that high bitrate does not always lead to high QoE (e.g., frequent bitrate switching annoys viewers [30], [48]).”
Copa: Practical Delay-Based Congestion Control for the Internet (2018, background): “have become far more sensitive to performance, with notions of “quality of experience” for real-time and streaming media [5, 39], and various metrics to measure Web performance being developed [14, 15, 4, 6, 30].”
CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction (2016, background): “The result shows that when the error is ≤ 20%, the n-QoE of MPC is close to optimal (>85%).”
A buffer-based approach to rate adaptation (2014, background): “Modeling user engagement is complex and on-going [4, 14].”
Understanding the impact of network dynamics on mobile video user engagement (2014, unknown): “However, these techniques use deep-packet-inspection to extract information beyond TCP/IP headers, which requires signi.cant computational resources to employ at the scale of network carriers and can pose privacy problems in practice.”
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions (2017, methodology): “Previous QoS criteria which consist of packet loss rate, network latency, peak signal-to-noise ratio (SNR) and RTT are not sufﬁcient for streaming media on Internet, and therefore, users’ perceived satisfaction (i.e. QoE) needs to be addressed [103], [104].”
Flare: Practical Viewport-Adaptive 360-Degree Video Streaming for Mobile Devices (2018, background): “However, the impact of such quality changes on QoE and QoE metrics for 360° videos in general are still under-explored, compared to the well-studied QoE metrics for regular videos [22].”