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
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On the Efficiency and Fairness of Multiplayer HTTP-based Adaptive Video Streaming
TL;DR: In this article, a non-linear MPC-based, router-assisted bandwidth allocation algorithm is proposed to maximize the user-perceived quality-of-experience (QoE) in adaptive video streaming.
Enabling Premium Service for Streaming Video in Cellular Networks
TL;DR: This paper designs and implements a network adaptation scheme called SHADE, which allocates limited transmission resources at the base station among applications smartly, and can significantly improve three key streaming video application QoE metrics simultaneously (up to 10 times improvement), compared to current practice.
DissertationDOI
Resource Allocation in Smart Infrastructure: Case Studies in Video Delivery and Electric Power Networks
TL;DR: In this article, a case-study-based approach is employed on two representative smart infrastructures: Internet video delivery and electric power networks, where client-side video players adapt video quality based on application layer protocol (MPEG-DASH) to optimize users quality of experience.
Posted Content
Poor Video Streaming Performance Explained (and Fixed)
TL;DR: This work carefully model and characterize the behavior of streaming video according to download size and network conditions, and uses this to develop an adaptive algorithm for optimally controlling download behavior, which achieves near-optimal throughput and fast bitrate adaptation, regardless of the control plane algorithm.
References
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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.