Zhuqi Li will present his FPO "Cross-layer Optimization for Video Delivery on Wireless Networks" on Thursday, January 26, 2023 at 3pm in CS 402
Zhuqi Li will present his FPO "Cross-layer Optimization for Video Delivery on Wireless Networks" on Thursday, January 26, 2023 at 3pm in CS 402 The members of his committee are as follows: Examiners: Kyle Jamieson (adviser), Ravi Netravali, and Jennifer Rexford Readers: Amit Levy and Victor Bahl (Microsoft) A copy of his thesis will be available before the FPO upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Mobile video applications have gained increasing popularity and become part of everyone’s daily experience. The quality of video has a significant impact on both the quality of users’ experience for video streaming and the accuracy of video analytic systems, which further impact the application revenue. The challenge to building a consistently high-quality video delivery system lies in two aspects. On the application side, the emerging new video applications are evolving to become more user-interactive, where existing prefetch and buffering algorithms cannot work properly. On the network side, the wireless network itself is fundamentally dynamic and unreliable due to the multipath effect and interference on the wireless channel. In this thesis, we present cross-layer optimizations from the application layer, network layer, and physical layer to improve the quality of video streaming over wireless network with the design and implementation of the following systems: Dashlet, a short video streaming system tailored for a high quality of experience by adapting to dynamic user actions. Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users’ swipe statistics to determine the pre-buffering order and bitrate. Spider, a multi-hop, millimeter-wave (mmWave) wireless relay network design to maximize the video analytic accuracy for the delivered video. Spider integrates a low-latency Wi-Fi control plane with a mmWave relay data plane, allowing agile re-routing around blockages. Spider proposes a novel video bit-rate allocation algorithm coupled with a scalable routing algorithm that maximizes application-layer video analytics accuracy. LAIA, a system to programmable control the wireless channel so that the wireless network can achieve consistently high throughput for robust video delivery. With the programmable interface to control the wireless channel, LAIA can improve wireless channels on the fly for single- and multi-antenna links, as well as nearby networks operating on adjacent frequency bands. Putting it together, this thesis demonstrates a set of optimizations in different layers in through network stack for building a high quality and robustness wireless video delivery system. The extensive evaluation demonstrates a significant improvement on both quality of experience for video streaming and accuracy for video analytics.
Please note the time change from a previous announcement : Zhuqi Li will present his FPO "Cross-layer Optimization for Video Delivery on Wireless Networks" on Thursday, January 26, 2023 at 11am in CS 302 The members of his committee are as follows: Examiners: Kyle Jamieson (adviser), Ravi Netravali, and Jennifer Rexford Readers: Amit Levy and Victor Bahl (Microsoft) Zoom link: [ https://princeton.zoom.us/j/6871275896 | https://princeton.zoom.us/j/6871275896 ] A copy of his thesis will be available before the FPO upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Mobile video applications have gained increasing popularity and become part of everyone’s daily experience. The quality of video has a significant impact on both the quality of users’ experience for video streaming and the accuracy of video analytic systems, which further impact the application revenue. The challenge to building a consistently high-quality video delivery system lies in two aspects. On the application side, the emerging new video applications are evolving to become more user-interactive, where existing prefetch and buffering algorithms cannot work properly. On the network side, the wireless network itself is fundamentally dynamic and unreliable due to the multipath effect and interference on the wireless channel. In this thesis, we present cross-layer optimizations from the application layer, network layer, and physical layer to improve the quality of video streaming over wireless network with the design and implementation of the following systems: Dashlet, a short video streaming system tailored for a high quality of experience by adapting to dynamic user actions. Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users’ swipe statistics to determine the pre-buffering order and bitrate. Spider, a multi-hop, millimeter-wave (mmWave) wireless relay network design to maximize the video analytic accuracy for the delivered video. Spider integrates a low-latency Wi-Fi control plane with a mmWave relay data plane, allowing agile re-routing around blockages. Spider proposes a novel video bit-rate allocation algorithm coupled with a scalable routing algorithm that maximizes application-layer video analytics accuracy. LAIA, a system to programmable control the wireless channel so that the wireless network can achieve consistently high throughput for robust video delivery. With the programmable interface to control the wireless channel, LAIA can improve wireless channels on the fly for single- and multi-antenna links, as well as nearby networks operating on adjacent frequency bands. Putting it together, this thesis demonstrates a set of optimizations in different layers in through network stack for building a high quality and robustness wireless video delivery system. The extensive evaluation demonstrates a significant improvement on both quality of experience for video streaming and accuracy for video analytics. _______________________________________________ talks mailing list talks@lists.cs.princeton.edu To edit subscription settings or remove yourself, use this link: https://lists.cs.princeton.edu/mailman/listinfo/talks
participants (1)
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Nicki Mahler