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Streaming video applications on the Internet generally have very high bandwidth requirements and yet are often unresponsive to network congestion. In order to avoid congestion collapse and improve video quality, these applications need to respond to congestion in the network by deploying mechanisms to reduce their bandwidth requirements under conditions of heavy load. In reducing bandwidth, video with high motion will look better if all the frames are kept but the frames have low quality, while video with low motion will look better if some frames are dropped but the remaining frames have high quality. Unfortunately, current video applications scale to fit the available bandwidth without regard to the video content. In this paper, we present a content -aware scaling mechanism that reduces the bandwidth occupied by an application by either dropping frames (temporal scaling) or by reducing the quality of the frames transmitted (quality scaling). We have designed a streaming video client and server with the server capable of quantifying the amount of motion in an MPEG stream and scaling each scene either temporally or by quality as appropriate, maximizing the quality of each video stream. We have evaluated our setup by conducting a user study wherein the subjects rated the quality of the video clips that were first scaled temporally and then scaled by quality in order to establish the optimal mechanism for scaling a particular stream. We find that our content-aware scaling can improve perceived video quality by as much as 50%. We have also evaluated the practical impact of adaptively scaling the video stream by conducting a user study for longer video clips with varying amounts of motion and available bandwidth. We find that for such clips the improvement in perceptual quality on account of adaptive content-aware scaling is as high as 30%.