The rapid growth of live video streaming on platforms such as Twitch and YouTube Live, driven by social media, professional sports, and video games, has created challenges for network management. Short-time-scale network congestion can significantly impact the quality of experience (QoE) for users watching live streams since client playback buffers are often no more than a few seconds. However, identifying live streams and measuring their QoE is challenging since content providers use the same delivery infrastructure for both live and video-on-demand (VoD) streaming. Packet inspection techniques such as SNI/DNS query monitoring cannot always distinguish between the two. To address this challenge, a team of researchers designed, built, and deployed ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics. The team analyzed approximately 23,000 video streams from Twitch and YouTube to identify key features in their traffic profile that differentiate live and on-demand streaming. They released their traffic traces as open data to the public. The researchers developed an LSTM-based binary classifier model that distinguishes live from on-demand streams in real-time with over 95% accuracy across providers. They also developed a method that estimates QoE metrics of live streaming flows in terms of resolution and buffer stall events with overall accuracies of 93% and 90%, respectively. Finally, the team prototyped their solution, trained it in the lab, and deployed it in a live ISP network serving more than 7,000 subscribers. Their method provides ISPs with fine-grained visibility into live video streams enabling them to measure and improve user experience without introducing any risk to the operational network. The system architecture consists of upstream and downstream traffic received on separate optical tap links processed by a Linux server running Ubuntu 18.04 with DPDK support for high-speed packet processing. The tool FlowFetch interacts with DPDK to fetch raw packets executing telemetry functions to export request packet counters and chunk features. The flow of events in the system involves detecting flows carrying video streams originating from Twitch and YouTube by performing pattern matches on the SNI field present in the TLS handshake.
Live video streaming is when people watch videos in real-time on platforms like Twitch and YouTube Live. Sometimes, the quality of the video can be affected by network congestion, which means too many people are trying to watch at once. It's hard for companies to tell if a video is being watched live or not because they use the same system for both live and pre-recorded videos. Researchers made a computer program called ReCLive that can tell if a video is live or pre-recorded with over 95% accuracy. They also made a way to measure how good the quality of the video is while it's being watched live. The program was tested on a real network with lots of people using it and it worked well without causing any problems.
Definitions
- Live video streaming: watching videos in real-time as they happen
- Quality of experience (QoE): how good something looks or works
- Network congestion: too many people using the internet at once, making it slow down
- Video-on-demand (VoD) streaming: watching pre-recorded videos whenever you want
- Machine learning: teaching computers to learn from data and make decisions based on that information
- Binary classifier model: a computer program that can sort things into two groups (in this case, live or pre-recorded)
- LSTM-based: a type of machine learning algorithm used for predicting sequences of data
- ISP network: an internet service provider's network, which provides internet access to customers
- Operational network
ReCLive: A Machine Learning Method for Live Video Detection and QoE Measurement
The rapid growth of live video streaming on platforms such as Twitch and YouTube Live, driven by social media, professional sports, and video games, has created challenges for network management. Short-time-scale network congestion can significantly impact the quality of experience (QoE) for users watching live streams since client playback buffers are often no more than a few seconds. However, identifying live streams and measuring their QoE is challenging since content providers use the same delivery infrastructure for both live and video-on-demand (VoD) streaming. Packet inspection techniques such as SNI/DNS query monitoring cannot always distinguish between the two. To address this challenge, a team of researchers designed, built, and deployed ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics.
Data Collection
The team analyzed approximately 23,000 video streams from Twitch and YouTube to identify key features in their traffic profile that differentiate live from on-demand streaming. They released their traffic traces as open data to the public.
Live Stream Detection Model
The researchers developed an LSTM-based binary classifier model that distinguishes live from on-demand streams in real time with over 95% accuracy across providers. They also developed a method that estimates QoE metrics of live streaming flows in terms of resolution and buffer stall events with overall accuracies of 93% and 90%, respectively.
Prototype Deployment
Finally, the team prototyped their solution, trained it in the lab, and deployed it in a live ISP network serving more than 7,000 subscribers. Their method provides ISPs with fine-grained visibility into live video streams enabling them to measure and improve user experience without introducing any risk to the operational network. The system architecture consists of upstream and downstream traffic received on separate optical tap links processed by a Linux server running Ubuntu 18.04 with DPDK support for high speed packet processing. The tool FlowFetch interacts with DPDK to fetch raw packets executing telemetry functions to export request packet counters & chunk features . The flow of events in the system involves detecting flows carrying video streams originating from Twitch & YouTube by performing pattern matches on SNI field present in TLS handshake .
Conclusion
In conclusion , ReCLive is an effective machine learning method for detecting & measuring Quality Of Experience (QOE ) metrics associated with Live Video Streaming . It enables ISPs to gain better visibility into these types of services which helps them improve user experience without risking operational networks .
Social media, professional sports, and video games are driving rapid growth in live video streaming, on platforms such as Twitch and YouTube Live. Live streaming experience is very susceptible to short-time-scale network congestion since client playback buffers are often no more than a few seconds. Unfortunately, identifying such streams and measuring their QoE for network management is challenging, since content providers largely use the same delivery infrastructure for live and video-on-demand (VoD) streaming, and packet inspection techniques (including SNI/DNS query monitoring) cannot always distinguish between the two. In this paper, we design, build, and deploy ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics. Our contributions are four-fold: (1) We analyze about 23,000 video streams from Twitch and YouTube, and identify key features in their traffic profile that differentiate live and on-demand streaming. We release our traffic traces as open data to the public; (2) We develop an LSTM-based binary classifier model that distinguishes live from on-demand streams in real-time with over 95% accuracy across providers; (3) We develop a method that estimates QoE metrics of live streaming flows in terms of resolution and buffer stall events with overall accuracies of 93% and 90%, respectively; and (4) Finally, we prototype our solution, train it in the lab, and deploy it in a live ISP network serving more than 7,000 subscribers. Our method provides ISPs with fine-grained visibility into live video streams, enabling them to measure and improve user experience.
