FastTrack: Minimizing Stalls for CDN-based Over-the-top Video Streaming Systems

TL;DR

This paper introduces a comprehensive model and an efficient algorithm to minimize stall durations in CDN-based video streaming, significantly improving user experience and system performance.

Contribution

It presents a new system model considering multiple factors and a novel algorithm for optimizing stall duration tail probability in video streaming systems.

Findings

Proposed algorithms significantly reduce stall duration tail probability.

Theoretical bounds for SDTP are established.

Real-world implementation validates simulation results.

Abstract

Traffic for internet video streaming has been rapidly increasing and is further expected to increase with the higher definition videos and IoT applications, such as 360 degree videos and augmented virtual reality applications. While efficient management of heterogeneous cloud resources to optimize the quality of experience is important, existing work in this problem space often left out important factors. In this paper, we present a model for describing a today's representative system architecture for video streaming applications, typically composed of a centralized origin server and several CDN sites. Our model comprehensively considers the following factors: limited caching spaces at the CDN sites, allocation of CDN for a video request, choice of different ports from the CDN, and the central storage and bandwidth allocation. With the model, we focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel, yet efficient, algorithm to solve the formulated optimization problem. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. Our extensive simulation results demonstrate that the proposed algorithms can significantly improve the SDTP metric, compared to the baseline strategies. Small-scale video streaming system implementation in a real cloud environment further validates our results.