Adaptive Resource Allocation with SVM-based Multi-hop Video Packet Delay Bound Violation Modeling

In this work, we develop a multi-hop packet delay bound violation model using Support vector machines (SVM) to predict the packet loss probability and end-to-end distortion for video streaming over multi-hop networks. Based on this model, we formulate the resource allocation into a non-convex optimization problem which aims to minimize the overall video distortion while maintaining fairness between sessions. We solve this optimization problem using Lagrangian duality methods. Extensive experimental results demonstrate that, with this widely-used offline-training-online-estimation mechanism, the proposed model is potentially applicable to almost all network conditions and can provide fairly accurate estimation results as compared with other models with a given sample data set. The proposed optimization algorithm achieves more efficient resource allocation than existing schemes.