Rate-distortion analysis and traffic modeling of scalable video coders

In this work, we focus on two important goals of the transmission of scalable video over the Internet. The first goal is to provide high quality video to end users and the second one is to properly design networks and predict network performance for video transmission based on the characteristics of existing video traffic. Rate-distortion (R-D) based schemes are often applied to improve and stabilize video quality; however, the lack of R-D modeling of scalable coders limits their applications in scalable streaming. Thus, in the first part of this work, we analyze R-D curves of scalable video coders and propose a novel operational R-D model. We evaluate and demonstrate the accuracy of our R-D function in various scalable coders, such as Fine Granular Scalable (FGS) and Progressive FGS coders. Furthermore, due to the time-constraint nature of Internet streaming, we propose another operational R-D model, which is accurate yet with low computational cost, and apply it to streaming applications for quality control purposes. The Internet is a changing environment; however, most quality control approaches only consider constant bit rate (CBR) channels and no specific studies have been conducted for quality control in variable bit rate (VBR) channels. To fill this void, we examine an asymptotically stable congestion control mechanism and combine it with our R-D model to present smooth visual quality to end users under various network conditions. Our second focus in this work concerns the modeling and analysis of video traffic, which is crucial to protocol design and efficient network utilization for video transmission. Although scalable video traffic is expected to be an important source for the Internet, we find that little work has been done on analyzing or modeling it. In this regard, we develop a frame-level hybrid framework for modeling multi-layer VBR video traffic. In the proposed framework, the base layer is modeled using a combination of wavelet and time-domain methods and the enhancement layer is linearly predicted from the base layer using the cross-layer correlation

Congestion-Aware Scalable Video Streaming

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Q-AIMD: A Congestion Aware Video Quality Control Mechanism

Following the constant increase of the multimedia traffic, it seems necessary to allow transport protocols to be aware of the video quality of the transmitted flows rather than the throughput. This paper proposes a novel transport mechanism adapted to video flows. Our proposal, called Q-AIMD for video quality AIMD (Additive Increase Multiplicative Decrease), enables fairness in video quality while transmitting multiple video flows. Targeting video quality fairness allows improving the overall video quality for all transmitted flows, especially when the transmitted videos provide various types of content with different spatial resolutions. In addition, Q-AIMD mitigates the occurrence of network congestion events, and dissolves the congestion whenever it occurs by decreasing the video quality and hence the bitrate. Using different video quality metrics, Q-AIMD is evaluated with different video contents and spatial resolutions. Simulation results show that Q-AIMD allows an improved overall video quality among the multiple transmitted video flows compared to a throughput-based congestion control by decreasing significantly the quality discrepancy between them