Name
Abstract | ||
|---|---|---|
ABSTRACTStreaming 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%, 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3458306.3460998 | ACM Conferences |
Keywords | DocType | Citations |
360 degrees videos, Encrypted data, Mobile Network Operators, ML classification | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chamara Kattadige | 1 | 1 | 1.70 |
| Aravindh Raman | 2 | 36 | 6.25 |
| Kanchana Thilakarathna | 3 | 6 | 6.88 |
| Andra Lutu | 4 | 98 | 18.25 |
| Diego Perino | 5 | 740 | 50.54 |