Adaptive Content-Aware Scaling for Improved Video Streaming

Tripathi, Avanish

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Adaptive Content-Aware Scaling for Improved Video Streaming

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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 thesis, we present an adaptive 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 appearance of each video stream. We have evaluated the impact of content-aware scaling by conducting a user study wherein the subjects rated the quality of video clips that were first scaled temporally and then by quality in order to establish the optimal mechanism for scaling a particular stream. We find that content-aware scaling can improve 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 also the improvement in perceptual quality on account of adaptive content-aware scaling is as high as 30%

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