360NorVic: 360-Degree Video Classification from Mobile Encrypted Video Traffic
| Autor: | Diego Perino, Andra Lutu, Chamara Kattadige, Aravindh Raman, Kanchana Thilakarathna |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Degree (graph theory) Network packet business.industry Computer science Real-time computing Encryption Multimedia (cs.MM) Identification (information) Cellular network Quality of experience Latency (engineering) business Mobile device Computer Science - Multimedia |
| Zdroj: | NOSSDAV |
| Popis: | 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. Comment: 7 pages, 15 figures, accepted in Workshop on Network and OperatingSystem Support for Digital Audio and Video (NOSSDAV 21) |
| Databáze: | OpenAIRE |
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