# iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking

arXiv: https://arxiv.org/abs/1804.09914 · HTML: https://mlsanity.com/paper/1804.09914/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [Machine Learning Systems: A Survey from a Data-Oriented Perspective](https://mlsanity.com/paper/2302.04810/) (2023)
- [Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation](https://mlsanity.com/paper/2112.02637/) (2021)
- [From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini](https://mlsanity.com/paper/2510.11269/) (2025)

## Abstract (arXiv)

Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present em iTelescope, the first intelligent, inexpensive, and scalable SDN-based solution for identifying and classifying video flows in real-time. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95% accuracy in identifying and classifying video streams from many providers including Youtube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.