360NorVic: 360-Degree Video Classification from Mobile Encrypted Video Traffic

TL;DR

This paper introduces 360NorVic, a machine learning-based system that accurately classifies 360-degree videos from encrypted mobile traffic, aiding network optimization and QoE improvement.

Contribution

It presents a novel ML-based classification engine capable of identifying 360-degree videos from encrypted traffic with high accuracy in near-realtime and offline modes.

Findings

Over 95% accuracy at packet level

More than 92% accuracy at flow level

Effective in commercial network deployment

Abstract

Streaming 360{\deg} video demands high bandwidth and low latency, and poses significant challenges to Internet Service Providers (ISPs) and Mobile Network Operators (MNOs). The identification of 360{\deg} video traffic can therefore benefits fixed and mobile carriers to optimize their network and provide better Quality of Experience (QoE) to the user. However, end-to-end encryption of network traffic has obstructed identifying those 360{\deg} videos from regular videos. As a solution this paper presents 360NorVic, a near-realtime and offline Machine Learning (ML) classification engine to distinguish 360{\deg} videos from regular videos when streamed from mobile devices. We collect packet and flow level data for over 800 video traces from YouTube & Facebook accounting for 200 unique videos under varying streaming conditions. Our results show that for near-realtime and offline classification at packet level, average accuracy exceeds 95%, and that for flow level, 360NorVic achieves more than 92% average accuracy. Finally, we pilot our solution in the commercial network of a large MNO showing the feasibility and effectiveness of 360NorVic in production settings.