The Key Technology Of Adaptive Video Streaming In Mobile Content Centric Network

The large scale video streaming service is a hot topic in Internet application. More and more people watch videos on mobile devices.However, the unstable link in mobile network may lead to frequent playback freeze, high start-up delay, and low video quality. Content centric network (CCN) is a promising future Internet architecture,attracted more and more research interest for real-time applications. On the other hand, dynamic adaptive streaming via HTTP (DASH) allows consumers request video content with different bit rates, which greatly improve the quality of experience of customers. Combining with scalable video coding (SVC) , DASH technology can achieve better performance. However, existing DASH over CCN faces several challenges, including low efficiency bit rate selection, network congestion, and low cache hit ratio.To solve those problems, this thesis provides a serious of solutions:1) a novel DASH solution for mobile content centric network (CCMN),including a self-learning based bit rate adaptation strategy and a interest flooding control mechanism. 2) a priority measurement for video data transmission, considering SVC layer data type and content popularity. 3)a cache management for SVC-based DASH service over CCMN, which push selected SVC layer data packet to neighbor nodes.To evaluate the performance,a simulation platform is built based on NdnSIM. Simulation results show the proposed solutions improve the performance of current DASH service in CCMNs.