Rate-Selective Caching for Adaptive Streaming Over Information-Centric Networks

The growing demand for video content is reshaping our view of the current Internet, and mandating a fundamental change for future Internet paradigms. A current focus on Information-Centric Networks (ICN) promises a novel approach to intrinsically handling large content dissemination, caching and retrieval. While ubiquitous in-network caching in ICNs can expedite video delivery, a pressing challenge lies in provisioning scalable video streaming over adaptive requests for different bit rates. In this paper, we propose novel video caching schemes in ICN, to address variable bit rates and content sizes for best cache utilization. Our objective is to maximize overall throughput to improve the Quality of Service (QoS). In order to achieve this goal, we model the dynamic characteristics of rate adaptation, deriving caps on average delay, and propose DaCPlace which optimizes cache placement decisions. Building on DaCPlace, we further present a heuristic scheme, StreamCache, for low-overhead adaptive video caching. We conduct comprehensive simulations on NS-3 (specifically under the ndnSIM module). Results demonstrate how DaCPlace enables users to achieve the least delay per bit and StreamCache outperforms existing schemes, achieving near-optimal performance to DaCPlace.