Abstract
An ultra-densification suggestion has been made to meet the increased demand for the data needs in future. It is also used to slim the transmitting of the base station and refine the frequency reuse. The increase in size is anticipated but network traffic due to multimedia streaming and live streaming in 5G New Radio (NR) establishes various problems, such as Matching network operator supply, user anticipation, and difficulty in network planning and conservation. To ease the above-mentioned problems appropriate arrangement of Quality of Service (QoS)—Quality of Experience (QoE) correlation framework design is required. To measure the QoE functioning, Dynamic Adaptive Streaming over HTTP (DASH) for video and multimedia streaming usage in a real-time simulation scenario taken from 5G traces in static and mobility cases are used. Finally, a Supervised Machine Learning classification algorithm is used to forecast the user expectation which helps to enhance the QoE performance.
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Ajeyprasaath, K.B., Vetrivelan, P. (2023). A QoE Framework for Video Services in 5G Networks with Supervised Machine Learning Approach. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_56
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DOI: https://doi.org/10.1007/978-981-99-0047-3_56
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