To optimize scarce network resources and present the highest quality video, streaming video systems need adapt to the video content as well as the network conditions. This paper presents ARMOR, a video streaming system that dynamically adjusts repair and media scaling to meet current video and network conditions. In order to adapt effectively, ARMOR, and any dynamic video adaptation system, needs to predict the video quality as perceived by end users over the range of scaling and repair choices. Thus, this paper first proposes a novel video quality metric called distorted playable frame rate that provides estimation of user perceptual quality considering temporal and quality degradations. Comprehensive user studies show distorted playable frame rate is more accurate than other video quality metrics. Analytic experiments with distorted playable frame rate and the ARMOR optimization algorithm illustrate the predictive power of the metric in a dynamic, streaming video system. Lastly, implementation and experiments of a complete, fully-functioning ARMOR system show the effective practicality of the proposed approach.

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