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arXiv · 2105.03611

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

Chamara Kattadige·Aravindh Raman·Kanchana Thilakarathna·Andra Lutu·Diego Perino
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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.

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Chamara Kattadige, Aravindh Raman, Kanchana Thilakarathna, Andra Lutu, Diego Perino. 2021-05-08. 360NorVic: 360-Degree Video Classification from Mobile Encrypted Video Traffic. https://doi.org/10.1145/3458306.3460998

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