Modeling, identifying, and emulating dynamic adaptive streaming over HTTP

As HTTP-based streaming video applications have grown to become a major source of Internet traffic, and as the new ISO standard Dynamic Adaptive Streaming over HTTP (DASH) gains industry acceptance, researchers need the ability to both (i) study real-world viewing data and (ii) emulate realistic DASH streams in network experiments. The first effort is complicated by the fact that researchers are often restricted to anonymized, header-only (i.e. payload-truncated) traces. The second effort is difficult since the process of encoding videos for DASH results in numerous large files and since popular videos are subject to restrictive copyright law. In this thesis we present our work towards developing a model for DASH traffic and show how the model can be applied to identify specific DASH videos in anonymized, header-only traces. We also present our solution for emulating DASH using compact representations of both DASH services (e.g. Netflix and Amazon) and videos.Master of Scienc

Traffic Profiling for Mobile Video Streaming

This paper describes a novel system that provides key parameters of HTTP Adaptive Streaming (HAS) sessions to the lower layers of the protocol stack. A non-intrusive traffic profiling solution is proposed that observes packet flows at the transmit queue of base stations, edge-routers, or gateways. By analyzing IP flows in real time, the presented scheme identifies different phases of an HAS session and estimates important application-layer parameters, such as play-back buffer state and video encoding rate. The introduced estimators only use IP-layer information, do not require standardization and work even with traffic that is encrypted via Transport Layer Security (TLS). Experimental results for a popular video streaming service clearly verify the high accuracy of the proposed solution. Traffic profiling, thus, provides a valuable alternative to cross-layer signaling and Deep Packet Inspection (DPI) in order to perform efficient network optimization for video streaming.Comment: 7 pages, 11 figures. Accepted for publication in the proceedings of IEEE ICC'1

Quality of Experience estimation for Adaptive HTTP/TCP video streaming using H.264/AVC

International audienceVideo services are being adopted widely in both mobile and fixed networks. For their successful deployment, the content providers are increasingly becoming interested in evaluating the performance of such traffic from the final users' perspective, that is, their Quality of Experience (QoE). For this purpose, subjective quality assessmentmethods are costly and can not be used in real time. Therefore, automatic estimation of QoE is highly desired. In this paper, we propose a noreference QoE monitoringmodule for adaptive HTTP streaming using TCP and the H.264 video codec. HTTP streaming using TCP is the popular choice of many web based and IPTV applications due to the intrinsic advantages of the protocol. Moreover, these applications do not suffer from video data loss due to the reliable nature of the transport layer. However, there can be playout interruptions and if adaptive bitrate video streaming is used then the quality of video can vary due to lossy compression. Our QoE estimation module, based on Random Neural Networks, models the impact of both factors. The results presented in this paper show that our model accurately captures the relation between them and QoE

Saving Energy in Mobile Devices for On-Demand Multimedia Streaming -- A Cross-Layer Approach

This paper proposes a novel energy-efficient multimedia delivery system called EStreamer. First, we study the relationship between buffer size at the client, burst-shaped TCP-based multimedia traffic, and energy consumption of wireless network interfaces in smartphones. Based on the study, we design and implement EStreamer for constant bit rate and rate-adaptive streaming. EStreamer can improve battery lifetime by 3x, 1.5x and 2x while streaming over Wi-Fi, 3G and 4G respectively.Comment: Accepted in ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP), November 201