QoE-Aware Multi-Source Video Streaming in Content Centric Networks

Mohammad Nazmus Sadat, Rui Dai, Lingchao Kong, Jingyi Zhu

Abstract: Content Centric Networking (CCN), a future Internet architecture, brings new challenges in maintaining the Quality of Experience (QoE) for video streaming. Because of the universal caching capability of CCN routers, streaming from multiple sources will be common, and switching between content sources might affect QoE by inducing delays and consequently stalls in video playback. This paper proposes a new QoE-aware multi-source video streaming scheme for CCN. First, the content distributions of video files among CCN nodes for different caching methods are studied. Second, an adaptive video streaming with distributed caching (ASDC) algorithm is designed to guarantee QoE during the switching between content sources. The ASDC algorithm considers the delivery of scalable video streams. It automatically adapts the layers in a video stream when there is source switching, based on a QoE model that characterizes the effect of stalling. Experimental results show that the ASDC algorithm outperforms dynamic adaptive streaming over HTTP (DASH) in the CCN platform in terms of the QoE obtained from human subjective tests.