Principal Investigator: Vaneet Aggarwal
Video streaming has become one of the most critical applications on the Internet. The demand for ondemand and live content (e.g., Netflix, Hulu, ESPN), as well as for user-generated content (e.g., YouTube) is increasing so rapidly that it is predicted that by 2019, 80% of the Internet traffic will be video, forming a $100 billion market. The key issues in video streaming involve novel scheduling algorithms for adptive-bitrate videos. Streaming video over cloud servers make the problem challenging. Further, 360-degree videos involve additional flexibilities of using head movement prediction for scheduling, which can be exploited.
Anis Elgabli, Muhamad Felemban, and Vaneet Aggarwal, "GiantClient: Video HotSpot for Multi-User Streaming," Accepted to IEEE Transactions on Circuits and Systems for Video Technology, Sept 2018.
Anis Elgabli, Vaneet Aggarwal, Shuai Hao, Feng Qian, and Subhabrata Sen, "LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming," IEEE/ACM Transactions on Networking, vol. 26, no. 4, pp. 1633-1645, Aug. 2018.
Abubakr Alabassi and Vaneet Aggarwal, "Video Streaming in Distributed Erasure-coded Storage Systems: Stall Duration Analysis," IEEE/ACM Transactions on Networking, vol. 26, no. 4, pp. 1921-1932, Aug. 2018.
Anis Elgabli, Vaneet Aggarwal, and Ke Liu, “Low Complexity Algorithm for Multi-path Video Streaming,” in Proc. IEEE SPCOM, Jul 2018
Anis Elgabli and Vaneet Aggarwal, "GroupCast: Preference-Aware Cooperative Video Streaming with Scalable Video Coding," in Proc. Infocom Workshop (International Workshop on Hot Topics in Pervasive Mobile and Online Social Networking (HotPOST'18)), Apr 2018 (Best paper award).
Keywords: cloud, multi-user, video streaming