An Empirical Analysis of Video Streaming and Congestion Control Models from a Pragmatic Perspective

Video streaming is a subfield of signal processing that encompasses the pre-processing of video sequences, their contextual segmentation, application-specific feature extraction and selection, and the detection of distinct frame sequences. Researchers suggest a broad range of machine learning models to develop such streaming approaches, and each model differs in its functional subtleties, application-specific benefits, deployment-specific limits, and contextual future possibilities. Moreover, these models differ in quantitative and qualitative metrics, such as streaming bit rate, computing complexity, and streaming latency. But most of these models are either highly complex, or do not support congestion mitigation for real-time traffic scenarios. Due to which, it is difficult for video streaming designers and research to identify optimal models for their specific use cases. To overcome this ambiguity, this text reviews existing video streaming models in terms of their functional characteristics. Based on this review, readers will be able to identify models that suit their functional requirements. This text also compares these models in terms of qualitative metrics including streaming delay, computational complexity, error rate during communication, scalability, and cost of deployment under real-time scenarios. Based on this analysis, researchers will be able to identify optimal models for their application-specific use cases. This text also proposes evaluation of a novel Video Streaming Rank Metric (VSRM) that combines these comparison metrics in order to identify an efficient set of optimal models for different contextual deployments.