An Efficient Video Streaming Architecture with QoS Control for Virtual Desktop Infrastructure in Cloud Computing

TL;DR

This paper proposes a new video streaming architecture for VDI in cloud computing that enhances QoS and QoE through a linear regression-based control system and a novel 2D image compression algorithm, validated by real dataset simulations.

Contribution

It introduces a flexible QoS control architecture with a novel image compression method tailored for real-time cloud-based remote desktop applications.

Findings

Improved QoS and QoE in VDI environments demonstrated through simulations.

Effective real-time image compression using k-means clustering.

Enhanced bandwidth management for high-quality remote desktop streaming.

Abstract

In virtual desktop infrastructure (VDI) environments, the remote display protocol has a big responsibility to transmit video data from a data center-hosted desktop to the endpoint. The protocol must ensure a high level of client perceived end-to-end quality of service (QoS) under heavy work load conditions. Each remote display protocol works differently depending on the network and which applications are being delivered. In healthcare applications, doctors and nurses can use mobile devices directly to monitor patients. Moreover, the ability to implement tasks requiring high consumption of CPU and other resources is applicable to a variety of applications including research and cloud gaming. Such computer games and complex processes will run on powerful cloud servers and the screen contents will be transmitted to the client. TO enable such applications, remote display technology requires further enhancements to meet more stringent requirements on bandwidth and QoS, an to allow realtime operation. In this paper, we present an architecture including flexible QoS control to improve the user quality of experience (QoE). The QoS control is developed based on linear regression modeling using historical network data. Additionally, the architecture includes a novel compression algorithm of 2D images, designed to guarantee the best image quality and to reduce video delay; this algorithm is based on k-means clustering and can satisfy the requirements of realtime onboard processing. Through simulations with a real work dataset collected by the MIT Computer Science and Artificial Lab, we present experimental as well as explain the performance of the QoS system.