Network-based video freeze detection and prediction in HTTP adaptive streaming

Given the popularity of HTTP adaptive streaming (HAS) technology for media delivery over mobile and fixed networks, the clients Quality of Experience (QoE) for HAS video sessions is of particular interest for network providers and Content Delivery Network (CDN) operators. Despite that, network providers are not able to directly obtain QoE-relevant metrics such as video freezes, initial buffering time, and the frequency of quality switches from the client. This paper proposes a scalable machine learning (ML) based scheme that offline detects and online predicts video freezes using a few features extracted from the network-based monitoring data, i.e., a sequence of HTTP GET requests sent from the video client. We determine the discriminative features for detecting video freezes based on multi-scale winch:11gs using the criterion of information gain (IG). Four traditional classifiers are investigated and the C4.5 decision tree is selected because of its simplicity, scalability, accuracy, and interpretability. Our approach for session based offline freeze detection is evaluated on the Apple HTTP Live Streaming video sessions obtained from a number of operational CDN nodes and on the traces of Microsoft Smooth Streaming video sessions acquired in a controlled lab environment. Experimental results show that, even with the disturbance of user interactivity, an accuracy of about 91% can be obtained for the detection of a video freeze, a long video freeze, and multiple video freezes. The experiments for the online freeze prediction show that more than 30% of the video freezes can be foreseen one segment in advance. (C) 2016 Published by Elsevier B.V

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

[EN] The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-tra_ c networks with multiple nodes/ sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G o_ ers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.Najm, IA.; Hamoud, AK.; Lloret, J.; Bosch Roig, I. (2019). Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics. 8(6):1-23. https://doi.org/10.3390/electronics8060607S12386Rahem, A. A. T., Ismail, M., Najm, I. A., & Balfaqih, M. (2017). Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET. 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