Gain Insights into Encrypted Adaptive Video Streaming
Unlock visibility into encrypted video traffic and deliver superior streaming experiences.
Video streaming now accounts for nearly 80% of global mobile data traffic, and most of it is transmitted in full encryption. Traditional monitoring methods no longer provide the visibility operators need to ensure Quality of Experience (QoE). At the same time, streaming remains one of the biggest drivers of customer engagement and revenue.
This white paper examines how RADCOM ACE leverages AI, machine learning, and advanced heuristics to deliver real-time insights into encrypted video sessions, enabling operators to enhance the customer experience on platforms such as YouTube, Netflix, Amazon Prime Video, and Facebook.
With encrypted adaptive streaming (HTTPS, QUIC, MPEG-DASH, HLS, etc.) becoming the industry norm, operators face the dual challenge of ensuring service quality while protecting privacy. This paper shows how to overcome that challenge with data-driven intelligence.
What You’ll Learn
- The streaming challenge: Why encryption makes it harder than ever to monitor video quality and user experience.
- Adaptive streaming explained: An overview of protocols like MPEG-DASH, HLS, and CMAF, and how they impact QoE.
- AI/ML-driven analysis: How RADCOM’s AI engine translates encrypted traffic into actionable Key Quality Indicators (KQIs).
- Essential video metrics: Learn how to measure startup delay, rebuffering events, resolution changes, throughput, and more.
- Quality scoring with vMOS: See how a composite QoE score provides clear visibility into the subscriber experience.
- Actionable insights: How operators can segment metrics by device, APN, location, and user type to drive smarter decisions.
- Business impact: How these insights help operators monetize video, reduce churn, and ensure competitive differentiation.
👉 Download the white paper to discover how AI-driven assurance empowers operators to deliver flawless video experiences in an encrypted world.
