CoMonDAS: A Framework for Efficient and Robust Dynamic Adaptive Streaming over NDN Implementing DASH, the most popular method for multimedia streaming, over NDN, a potential future Internet architecture, can substantially increase the network bandwidth utilization. However, inherent features of NDN can create new security risks for adaptive multimedia streaming. We propose a novel attack called Bitrate Oscillation Attack (BOA), which adversely exploits NDNxe2x80x99s autonomous onpath caching and interest aggregation to unsettle DASH functionality. BOA forces the resolution and quality of video received by the attacked client to oscillate with high frequency and amplitude. Subsequently, we present CoMonDAS, a framework for lightweight coordination that mitigates BOA and other attacks in NDN. Through extensive simulations, we demonstrate that BOA is very harmful for DAS over NDN, but can be significantly mitigated by CoMonDAS.

Exploiting the layer correlation to improve DASH scheduling with scalable video coding Abstract   DASH is a promising video streaming solution that gracefully deals with network throughput fluctuations as well as terminal heterogeneity. In DASH, an MPD file describes the available video qualities and their average bitrates. However, the coarsegrained average bitrate is insufficient for an accurate clientside scheduler. The bitrate overestimate or underestimate of bandwidth requirements for segments to be fetched could result in suboptimal scheduling decisions and possibly drain the playback buffer. In this paper, we first made the observation that with SVC, video segments across different layers exhibit high size correlation. We then showed that enhancement layer segment size can be predicted by the corresponding base layer segment with relatively high accuracy. Based on this observation, we enhanced existing ratebased and bufferedbased adaptive bitrate (ABR) algorithms with a size predictor, which is orthogonal to existing adaptation logics. Results show that augmented by the size predictor, the average playback bitrate can be improved by up to 20% and playback stalls can also be significantly reduced. Our results demonstrated that similar to the bandwidth predictor, a size predictor should also be added to SVCbased ABR algorithms to increase the performance gain.