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Research questionHow can we forecast bursty XR traffic and QoE risk from encrypted packet observations?XR traffic is bursty and non-stationary, making its future frame-level behavior difficult to predict. Encryption limits direct access to application semantics, while QoE risk must be inferred from observable packet patterns and timing.
AI
Machine Learning
Research Paper
Statistical Machine Learning
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Latest papersRecent research connected to this question, newest first.ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended RealityThe source presents evidence for residual learning across XR traffic forecasting and QoE-risk estimation, including a dataset pairing traffic traces with user-reported session-level QoE labels. Reported results cover frame-count, frame-size, inter-arrival-time, and QoE-risk prediction.research paper · Sep 3, 2026
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