Efficient Upstream Bandwidth Multiplexing for Cloud Video Recording Services

The upsurge of cloud video recording (CVR) has gained increasing attention from the general public and entrepreneurs. With live video records archived in the cloud, the CVR paradigm enables various smart services by keeping track of activities in the monitored region from anywhere at any time. However, the limited upstream bandwidth affects the quality of surveillance when multiple distributed cameras share the same upstream link. To solve the problem, this paper proposes an efficient upstream bandwidth multiplexing algorithm to intelligently allocate upstream bandwidth for each live video stream while maximizing the overall utility from the perspective of a CVR user. Specifically, we formulate the upstream bandwidth multiplexing problem as a constrained stochastic optimization problem, and apply the technique of hierarchical approximation to solve it efficiently. Our algorithm can be extended to take the priority of video streams into account and allocate more upstream bandwidth to video streams with higher priorities. We explicitly prove the approximation ratio of the proposed algorithm. In addition, we also conduct extensive trace-driven simulations to verify the effectiveness of our algorithm. The simulation results show that our algorithm improves the overall CVR user utility by over 20% compared with other alternatives, and the average utility per bandwidth unit is guaranteed to be stable even when the number of video streams increases.