In recent years, HTTP adaptive streaming (HAS) has attracted considerable attention as the state-of-the-art technology for video transport. HAS dynamically adjusts the quality of video streaming according to the network bandwidth and device capability of users. Content-Centric Networking (CCN) has also emerged as a future Internet architecture, which is a novel communication paradigm that integrates content delivery as a native network primitive. These trends have led to the new research issue of harmonizing HAS with the in-network caching provided by CCN routers.
Previous research has shown that the performance of HAS can be improved by using the H.264/SVC(scalable video codec) in the in-network caching environments.
However, the previous study did not address the misbehavior that causes video freeze when overestimating the available network bandwidth, which is attributable to the high cache hit rate.
Thus, this dissertation proposes a new SVC-based rate determination algorithm(RDA) that utilizes a drop timer. Our approach aims to stop the downloading of additional enhancement layers that are not cached in the local CCN routers in a timely manner, thereby preventing excessive consumption of the video buffer.
We implement our RDA in the SVC-HAS client and deployed a test-bed that could run Smooth-Streaming, which is one of the most popular HAS solutions, over CCNx, which is the reference implementation of CCN. Our experimental results show that the proposed RDA (SLA) could avoid video freeze in an effective manner, but without reducing the high hit rate on the CCN routers or affecting the high video quality on the SVC-HAS client.
Our second proposal is that of a new RDA called HAVS(Hybrid Adaptive Video Streaming), for mobile devices in wireless environment. The proposed approach takes two existing video streaming technologies: Progressive Download and Adaptive Streaming, and switches them in a hybrid manner.
HAVS employs the scalable video encoding scheme, where each video chunk is encoded into one base layer and several enhancement layers. Since clients request the base layer every time a video is streamed, HAVS performs Progressive Download for the base layer and Adaptive Streaming for the enhancement layers.
Wireless test-bed experiments have proved, that the HAVS method can be easily implemented in mobile devices without any server-side modification. This method effectively prevents video freeze thereby providing better quality of video streaming than the existing non-hybrid streaming technologies.
