Quality Control for Scalable Media Processing Applications
Journal of Scheduling
The Mathematics of Internet Congestion Control (Systems and Control: Foundations and Applications)
The Mathematics of Internet Congestion Control (Systems and Control: Foundations and Applications)
Rate-distortion optimized streaming of packetized media
IEEE Transactions on Multimedia
Analysis of video transmission over lossy channels
IEEE Journal on Selected Areas in Communications
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Optimal trellis-based buffered compression and fast approximations
IEEE Transactions on Image Processing
Towards network-wide QoE fairness using openflow-assisted adaptive video streaming
Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking
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In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.