Proceedings ArticleDOI
Confused, timid, and unstable: picking a video streaming rate is hard
Te-Yuan Huang,Nikhil Handigol,Brandon Heller,Nick McKeown,Ramesh Johari +4 more
- pp 225-238
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TLDR
This work measures three popular video streaming services -- Hulu, Netflix, and Vudu -- and finds that accurate client-side bandwidth estimation above the HTTP layer is hard, and rate selection based on inaccurate estimates can trigger a feedback loop, leading to undesirably variable and low-quality video.Abstract:
Today's commercial video streaming services use dynamic rate selection to provide a high-quality user experience. Most services host content on standard HTTP servers in CDNs, so rate selection must occur at the client. We measure three popular video streaming services -- Hulu, Netflix, and Vudu -- and find that accurate client-side bandwidth estimation above the HTTP layer is hard. As a result, rate selection based on inaccurate estimates can trigger a feedback loop, leading to undesirably variable and low-quality video. We call this phenomenon the "downward spiral effect", and we measure it on all three services, present insights into its root causes, and validate initial solutions to prevent it.read more
Citations
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Journal ArticleDOI
A Survey on Quality of Experience of HTTP Adaptive Streaming
TL;DR: The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation.
Journal ArticleDOI
Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale
TL;DR: It is argued that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation, which is akin, but also orthogonal to the transport-layer TCP congestion control, and PANDA - a client-side rate adaptation algorithm for HAS is presented.
Journal ArticleDOI
Review of Internet of Things (IoT) in Electric Power and Energy Systems
TL;DR: An assessment of the role, impact and challenges of IoT in transforming EPESs is provided and several opportunities for growth and development are offered.
Journal ArticleDOI
Improving Fairness, Efficiency, and Stability in HTTP-Based Adaptive Video Streaming With Festive
TL;DR: A principled understanding of bit-rate adaptation is presented and a suite of techniques that can systematically guide the tradeoffs between stability, fairness, and efficiency are developed, which lead to a general framework for robust video adaptation.
Proceedings ArticleDOI
Towards network-wide QoE fairness using openflow-assisted adaptive video streaming
TL;DR: An OpenFlow-assisted QoE Fairness Framework is proposed that aims to fairly maximise theQoE of multiple competing clients in a shared network environment by leveraging a Software Defined Networking technology, such as OpenFlow, that provides a control plane that orchestrates this functionality.
References
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TCP Congestion Control
TL;DR: This document defines TCP's four intertwined congestion control algorithms: slow start, congestion avoidance, fast retransmit, and fast recovery, as well as discussing various acknowledgment generation methods.
Proceedings ArticleDOI
Youtube traffic characterization: a view from the edge
TL;DR: This paper presents a traffic characterization study of the popular video sharing service, YouTube, and finds that as with the traditional Web, caching could improve the end user experience, reduce network bandwidth consumption, and reduce the load on YouTube's core server infrastructure.
Proceedings ArticleDOI
An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP
TL;DR: This paper focuses on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluates two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF).
Proceedings ArticleDOI
Understanding the impact of video quality on user engagement
Florin Dobrian,Vyas Sekar,Asad Awan,Ion Stoica,Dilip Antony Joseph,Aditya Ravikumar Ganjam,Jibin Zhan,Hui Zhang +7 more
TL;DR: This paper uses a unique dataset that spans different content types, including short video on demand, long VoD, and live content from popular video con- tent providers, to measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.
Proceedings ArticleDOI
Unreeling netflix: Understanding and improving multi-CDN movie delivery
Vijay Kumar Adhikari,Yang Guo,Fang Hao,Matteo Varvello,Volker Hilt,Moritz Steiner,Zhi-Li Zhang +6 more
TL;DR: A measurement study of Netflix is performed to uncover its architecture and service strategy, and finds that Netflix employs a blend of data centers and Content Delivery Networks (CDNs) for content distribution.