PPNet: Privacy Protected CDN-ISP Collaboration for QoS-aware Multi-CDN Adaptive Video Streaming

Abstract

Software-defined networking introduces opportunities to optimize the Internet Service Provider's network and to improve client experience for the Video-on-Demand applications. Recent studies on SDN frameworks show that traffic engineering methods allow a fair share of bandwidth between adaptive video streaming clients. Additionally, ISPs can make better estimations of bandwidth and contribute to the bitrate selection for the clients. This study focuses on another aspect of network assistance in video delivery: CDN server selection. In a typical framework where the ISP contributes to the CDN selection, the video provider and the network provider interfaces are merged together. Clients connect to the ISP to get the best CDN server candidate for a given video. This exposes client requests to the ISP. However, video providers have been investing large resources for encrypted video provisioning to preserve their client's information from third parties, especially network providers. The typical approach is not practical due to privacy concerns. In this study, we present a framework called PPNet to allow CDN-ISP collaboration while preventing the ISP's access to the video request and availability information. Our framework introduces an isolation between the video provider's and the ISP's web interfaces. Clients connect to both of the interfaces and deliver information on a need-to-know basis. As a second contribution, PPNet introduces a practical optimization method for CDN selection. Real-time data collection capabilities of a typical OpenFlow network is used as the input for optimization. Congestion-awareness has been the priority. To adapt for changing network conditions, capability of utilizing multiple servers simultaneously for a single video is introduced. Instead of directing each video client into a CDN node, the proposed system performs request routing per video segment. Finally, we present a system prototype of PPNet and show that our multiple-host adaptive streaming method introduces a significant improvement in quality of experience when compared to the state of the art.