IJFCC 2024 Vol.13(2): 37-43
DOI: 10.18178/ijfcc.2024.13.2.615
Big Data: Real-Time Video Streaming and Log Analytic for Improving Quality of Experience
Reza S. Kalan
Manuscript received March 1, 2024; revised April 5, 2024; accepted May 10, 2024; published June 3, 2024.
Abstract—Client-side adaptive bitrate algorithms are designed to optimize human-perceived Quality of Experience (QoE). However, network heterogeneity at the edge makes it difficult to provide the same video quality to all end users. Even the best Content Delivery Networks (CDNs) or Internet Service Providers (ISPs) have poor quality in certain regions or times of the day. In addition to network dynamism, online clients continuously switch between video channels that stream via different CDNs. The volume of video logs and network dynamics make it very difficult to analyze client-side video quality or monitor network performance and thus make timely decisions. The concept of big data analytics is a successful and cost-effective data mining tool and application that offers deep analytics, high agility, and massive scalability with low latency. Recently, with the advent of distributed computing technologies, the analysis of big video data in the cloud has attracted the attention of researchers and practitioners. Resource-rich edge or cloud servers have become popular destinations for video streaming and log analytics. In this paper, we discuss the change in the requirements for video streaming and illustrate the difficulty of big data log analytics at the edge. We then show the advantage of log analytics in the cloud and its impact on improving users’ QoE and reducing CDN traffic distribution costs by detecting and removing illegal streaming along with CDN switching.
Keywords—big data, log analytic, adaptive video streaming, quality of experience
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Cite: Reza S. Kalan, "Big Data: Real-Time Video Streaming and Log Analytic for Improving Quality of Experience," International Journal of Future Computer and Communication, vol. 13, no. 2, pp. 37-45, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)
