Unreeling netflix: Understanding and improving multi-CDN movie delivery
By Vijay Kumar Adhikari, Yang Guo, Fang Hao, Matteo Varvello, V. Hilt, Moritz Steiner, Zhi-Li Zhang. 2012 Proceedings IEEE INFOCOM, 2012
423 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/32be211e2f4164f5e1d6813c5a9ebab0e3f8be28
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Michael Walfish
- Srinath T. V. Setty
- Nick McKeown
- Ramesh Johari
- Te-Yuan Huang
- Brandon Heller
- Nikhil Handigol
- Yong Liu
In Computer Vision and Pattern Recognition
- Nick McKeown
- Ramesh Johari
- Te-Yuan Huang
- Michael Walfish
- Srinath T. V. Setty
- Yong Liu
- Róbert Pálovics
- Guibin Tian
Citations in the corpus
- Scalable and Private Media Consumption with Popcorn (2016, background): “The second threat considers a consumer that is trying to copy and redistribute content beyond what is allowed by the distributor.”
- Online Machine Learning in Big Data Streams (2018, background): “Recommendation models rely on the feedback provided by the user, which can be explicit, such as one to five star movie ratings on Netflix [4].”
- Confused, timid, and unstable: picking a video streaming rate is hard (2012, background): “In [1], the authors examine the CDN selection strategy of Hulu, while in [2], the authors look at Netflix.”
- Towards agile and smooth video adaptation in dynamic HTTP streaming (2012, background): “DASH WITH MULTIPLE SERVERS While most DASH services employ multiple servers hosting the same set of video contents, each client is only assigned to one server [1].”