360NorVic: 360-Degree Video Classification from Mobile Encrypted Video Traffic
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Streaming 360 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 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 videos from regular videos. As a solution this paper presents 360NorVic, a near-realtime and offline Machine Learning (ML) classification engine to distinguish 360 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 than 92 network of a large MNO showing the feasibility and effectiveness of 360NorVic in production settings.
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