Stanford University Puffer

Bonisiwe Shabane

-Dec 29, 2025, 8:30 AM

Stream live TV in your browser. There's no charge. You can watch U.S. TV stations affiliated with the NBC, CBS, ABC, PBS, Fox, and CW networks. Puffer works in the Chrome, Firefox, Edge, and Opera browsers, on a computer or an Android phone or tablet. Puffer does not work on iPhones or iPads or in Safari.

Puffer is a research project in the computer science department at Stanford University. Please find more details in the FAQ and our research paper (USENIX NSDI '20 Community Award, IRTF Applied Networking Research Prize '21). Puffer is a free and open-source live TV research study operated by Stanford University that uses machine learning to improve video streaming algorithms. It is written mostly in the C++ programming language [1] and relies on WebSocket as a transmission layer. [2] The study allows users across the United States to watch seven over-the-air television stations broadcasting in the San Francisco Bay Area media market for free.[3] Puffer was presumed to be launched on January 18, 2019.

It was initially led by Francis Yan, a Stanford computer science doctoral student, with Hudson Ayers and Sadjad Fouladi from Stanford, and Chenzhi Zhu from Tsinghua University. The project's facility advisors are professors Keith Winstein and Philip Levis.[4][5] The research study uses machine learning to improve video-streaming algorithms, such as those commonly used by services like YouTube, Netflix, and Twitch. The goal is to teach a computer to design new algorithms that reduce glitches and stalls in streaming video (especially over wireless networks and those with limited capacities, such as in rural areas), improve... The service is limited. Only those in the U.S. can sign up, and only up to 500 users can watch Puffer at a time.

In addition, the service only re-transmits free over-the-air television channels in the San Francisco Bay Area media market, specifically the following ones picked up by an antenna located on the Stanford campus: KTVU 2... The service is not compatible with Apple’s Safari browser or with iPhone and iPad devices because it relies on Media Source Extensions, which are not supported on those platforms. Puffer (puffer.stanford.edu) is a free and open-source live TV streaming website, and also a research study at Stanford University using machine learning to improve video streaming. More details can be found on the website, in our research paper (Community Award winner at NSDI 2020), and in the documentation. BY USING THIS WEBSITE AND ITS RELATED CONTENT AND SERVICES (COLLECTIVELY, THE "WEBSITE"), YOU AGREE TO THE TERMS DESCRIBED BELOW FOR YOUR USE OF THE WEBSITE AND YOUR PARTICIPATION IN THE STANFORD STUDY AS... If you do not agree to the terms of this agreement, please stop using this Website.

The Study You are invited to participate in an academic research study on improving the algorithms used for data transmission and video streaming over the Internet (the "Study"). The Website will stream live broadcast signals that the Stanford research team receives from our local digital antenna in Stanford, CA, to the study participants without any modification whatsoever to the programing content. The purpose of the Study is to figure out how to teach a computer to design new algorithms that reduce glitches and stalls in streaming video (especially over wireless networks and those with limited... As you stream video over your Internet connection, the Website will experiment with different algorithms that control the timing and quality of video sent to you. The Website will automatically collect information from your Web browser, including which video channel was sent to you, the picture quality of that video compared with the original version received by our antenna, the... You will not receive a survey or be required to answer any questions as part of the Study.

This Website was created by the Stanford researchers for the purpose of conducting this academic research Study, and will not be used for any commercial purpose. The information collected by the Website will be used for scientific purposes, including to draft scientific articles submitted for publication or presented at scientific or professional meetings, and to publish open-source software. You will not be individually identified in any publications or software released by the Stanford research team. There was an error while loading. Please reload this page. Puffer is a Stanford University research study about using machine learning to improve video-streaming algorithms: the kind of algorithms used by services such as YouTube, Netflix, and Twitch.

We are trying to figure out how to teach a computer to design new algorithms that reduce glitches and stalls in streaming video (especially over wireless networks and those with limited capacity, such as... Watch TV on this website. The idea of this study is streaming TV channels to study participants over the Internet, and the Puffer website will automatically experiment with different algorithms that control the timing and quality of video sent... The more diverse the Internet connections that the study participants use, the better the system will be able to learn, and the more robust the resulting computer-generated algorithms. Yes. Well, technically you don't even have to watch.

We just need people to stream video over different kinds of Internet connections, so that the computer has some live traffic to learn from and experiment on. Visit the Sign up page to join. You must be within the United States to use Puffer. No, there is no charge to participate. Puffer is an academic project at Stanford University and is entirely non-profit. Along with our research paper, we are publishing anonymized data collected on Puffer for the research community to investigate.

As our experiments are ongoing, new data is collected each day. This data is posted daily to the Experiment Results page, which also contains all data collected since the experiment began in January 2019. On this page, we provide a brief description. Please see the README in the puffer-statistics repo for more details on the data analysis. A single day of data is several GB, so please download a small set of fake data first to determine if the full Puffer data is indeed what you need. We would also be grateful if you could download the data from our server only once.

If anything is not clear in the below data description, please don't hesitate to post a question in our Google Group. At a high level, each day's Puffer data comprises different "measurements" — each measurement contains a different set of time-series data collected on Puffer servers, and is dumped as a CSV file. The CSV files that are essential for analysis include video_sent_X.csv, video_acked_X.csv, and client_buffer_X.csv, where X represents the day when the data was collected. For example, "2019-11-04T11_2019-11-05T11" means the data was collected between 2019-11-04T11:00:00Z and 2019-11-05T11:00:00Z (UTC is the default time zone). In addition to these three CSVs, we also release video_size_X.csv and ssim_X.csv that are described below. A special field in many CSV files is expt_id.

This is a unique ID identifying information associated with a "scheme", or pair of ABR and congestion control algorithms such as Fugu/BBR. The expt_id can be used as a key to retrieve the associated settings (e.g. algorithms and git commit) in the logs/expt_settings file. Each day has its own logs/expt_settings, containing the settings of all schemes run on Puffer between January 2019 and that day (as well as later days, if the analysis was performed later). If an expt_id were missing in the file, it would suggest an out-of-date file. The csv_to_stream_stats program in the puffer-statistics repo provides a function to parse this file.

Note that the research paper also uses the term "experiment" to refer to a group of schemes, e.g. the "primary experiment", whereas the expt_id refers to a single scheme. Additionally, there are two terms that we will use in the description: "stream" and "session". When a Puffer client watches TV for the first time or reloads the player page, it starts a new "session", identified by session_id in the CSVs. When a client switches channels, it enters into a different "stream" but still remains in the same "session", which uses the same TCP connection. Each CSV contains an index field solely used to group streams.

Two datapoints are considered part of the same stream if and only if they share both session_id and index. The values of session_id and index are not meaningful otherwise.

Stanford University Puffer

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