Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as re...Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.展开更多
Despite the success of MPEG?2 Transport Stream (TS) being used to deliver services in broadcast channels, the increase of on?demand viewing of multimedia content over IP with browser?centric media endpoints introduces...Despite the success of MPEG?2 Transport Stream (TS) being used to deliver services in broadcast channels, the increase of on?demand viewing of multimedia content over IP with browser?centric media endpoints introduces a new requirement for more indi?vidualized and flexible access to content. This has resulted in alternatives to MPEG?2 TS. While the needs of interactive broad?cast services (such as personalized advertisement or selection of audio stream with a language suitable for a specific user) grow there is an active standardization work under going for the next generation broadcasting systems. To best enable a complete sys?tem of hybrid broadcast and broadband services, Advanced Television Systems Committee (ATSC) 3.0 has developed an enhanced broadcast transport method named Real?Time Object Delivery over Unidirectional Transport (ROUTE)/DASH for delivery of DASH?formatted content and non?real time (NRT) data. Additionally, for broadcasting, ATSC 3.0 has also adopted MPEG Media Trans?port (MMT) standard, which inherits major advantageous features of MPEG?2 TS and is very useful in real?time streaming delivery via a unidirectional delivery network.This paper mainly describes features and design considerations of ATSC 3.0, and discusses the applications of the transport protocols used for broadcasting, i.e., ROUTE/DASH and MMT, whose comparative introductions are also presented in details.展开更多
基金This work was supported by a research grant from Seoul Women’s University(2023-0183).
文摘Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.
文摘Despite the success of MPEG?2 Transport Stream (TS) being used to deliver services in broadcast channels, the increase of on?demand viewing of multimedia content over IP with browser?centric media endpoints introduces a new requirement for more indi?vidualized and flexible access to content. This has resulted in alternatives to MPEG?2 TS. While the needs of interactive broad?cast services (such as personalized advertisement or selection of audio stream with a language suitable for a specific user) grow there is an active standardization work under going for the next generation broadcasting systems. To best enable a complete sys?tem of hybrid broadcast and broadband services, Advanced Television Systems Committee (ATSC) 3.0 has developed an enhanced broadcast transport method named Real?Time Object Delivery over Unidirectional Transport (ROUTE)/DASH for delivery of DASH?formatted content and non?real time (NRT) data. Additionally, for broadcasting, ATSC 3.0 has also adopted MPEG Media Trans?port (MMT) standard, which inherits major advantageous features of MPEG?2 TS and is very useful in real?time streaming delivery via a unidirectional delivery network.This paper mainly describes features and design considerations of ATSC 3.0, and discusses the applications of the transport protocols used for broadcasting, i.e., ROUTE/DASH and MMT, whose comparative introductions are also presented in details.
