Current Issue : October-December Volume : 2022 Issue Number : 4 Articles : 5 Articles
HTTP adaptive streaming (HAS) technologies such as dynamic adaptive streaming over HTTP (DASH) and common media application format (CMAF) are now used extensively to deliver live streaming services to large numbers of viewers. However, in dynamic networks, inaccurate bandwidth prediction may result in the wrong request of bitrate, and short-term network fluctuations may produce glitches, causing unnecessary bitrate switching, thereby degrading clients’ Quality of Experience (QoE). To tackle this, we propose adaptive bandwidth prediction and smoothing glitches in low-latency live streaming (called APSG) in this article. Concretely, firstly, the size of random bandwidth fluctuations is exploited as the weight of exponentially weighted moving average (EWMA) for adaptive bandwidth prediction; in addition to bandwidth prediction and buffer occupancy, glitches phenomena under a stable network environment are taken into account to enhance the viewing experience of clients. Finally, experimental results show that compared to traditional ABR algorithms under a stable network environment, APSG could reduce the number of bitrate switches and latency by up to 72.6% and 27.3%, respectively; under a dynamic network environment, APSG could reduce the number of bitrate switches and latency by up to 53.8% and 23.6%, respectively....
Given the massive popularity of digital music industry repositories and their corresponding targeting by cybercriminals, this paper presents an intelligent model for cyberattacks defense in digital music streaming platforms by mobile distributed machine learning. The basic idea of machine learning is to use large data sets to create a model that responds well to inputs it has never processed before. With the increase in data volume and complexity of models, it becomes increasingly challenging to complete machine learning processes in a single machine. Distributed ML was developed to solve this problem, and a standard procedure is completed through the collaboration of multiple servers. With the evolution of mobile devices and the increase in their number, it is possible to create an integrated and compact mobile distributed machine learning (MDML) system that could reduce the workload of servers. A distributed logit polynomial function model is proposed, which is used to model options in distributed binary regression accounting units, which are of low complexity and high stability in noisy environments....
In the information age, traditional teaching methods can no longer meet the needs of modern students, and the emergence of multimedia courseware teaching mode just solves this problem. Under the trend of education integration, multimedia courseware is becoming more and more important in classroom teaching. Aiming at the characteristics of AI multimedia courseware classroom teaching mode, we discuss the practical application of AI multimedia courseware in classroom teaching and study the classroom teaching mode. Building a distance education platform through network technology can promote the education level. At the same time, the interactive distance education platform serves as a new network platform for system users in the website. In order to speed up the process of education informationization and improve the quality of education, this paper uses .NET technology and video transmission technology to design and implement a multiperson video conversation interactive distance education platform. Through the comparative experiment of two classes in Chengguan Middle School, using this platform for teaching, the average grade of students and the average grade of each course are better than the average grade of nonplatform classes, and the student’s cognitive level has improved and is higher than the same level class. The results show that this platform can support a large number of online users and is very good for students of different majors. Through flexible and interactive course interaction, students are more convenient and more independent to learn but also reduce the teacher’s time cost and greatly play the advantages of distance education, and the English teaching mode of artificial intelligence multimedia courseware is very popular with students. More than 60% of teachers are satisfied with the teaching mode and above....
This study aimed to examine the association between Machiavellianism and gift-giving in live video streaming, as well as the mediating role of desire for control and the moderating role of materialism in this relation. A sample of 212 undergraduate students (146 males; the average age was 19.80 ± 2.05 years old) with experience of gift-giving in live video streaming was recruited to complete questionnaires on Machiavellianism, desire for control, materialism, and the frequency of gift-giving in live video streaming. The results showed that Machiavellianism was positively associated with gift-giving in live video streaming through the mediating role of desire for control; and the mediating effect of desire for control was moderated by materialism, with this relation being stronger for individuals with a higher level of materialism. Though with several limitations (e.g., cross-sectional method), this study could deepen our understanding of the influencing mechanism of gift-giving in live video streaming, which could also provide practical implications for the sustainable development of the live video streaming industry....
With the development of economy, more and more attention has been paid to the monitoring system, which provides a reliable and powerful guarantee for people’s daily life, property security, and national security. The intelligent video surveillance introduces computer vision-related technologies into traditional video surveillance and realizes the analysis and understanding of video data without artificial dependence to obtain valuable target information in the perceived video data. On this basis, functions such as abnormal event monitoring and real-time alarm are realized. Distributed streaming media monitoring has changed the manual-based monitoring and content analysis modes of traditional monitoring, but the high-complexity calculations such as motion estimation and motion compensation in the encoding process increase the burden of monitoring and sensing equipment. Especially with the development of wireless multimedia technology, the traditional video coding has been unable to meet the requirements of monitoring and sensing equipment in the monitoring system based on wireless technology. This paper proposes an adaptive weighted tensor completion algorithm to complete the repair of streaming media data perceived by ordinary sensing devices. In the proposed algorithm, considering the unbalanced information distribution and data redundancy problems that may exist in the data, the tensor data is adjusted according to the approximate solution algorithm to obtain tensor data that only retains important information and the information distribution is more balanced and reasonable. In the iterative solution process, in order to better map the impact of each dimension of data in the repair process, an adaptive weighting mechanism is proposed according to the data characteristics to obtain the corresponding weight value of each dimension of data. Finally, the proposed approximate tensor solving algorithm and adaptive weighting mechanism are applied to a simple low-rank tensor completeness algorithm based on tensor columns to form the algorithm of this paper, and it is used to repair perceptual streaming media data with data missing problems. The experimental results show that the algorithm in this paper can improve the perceived streaming media data quality by 3% based on the known data information and maintain an advantage of 2% in average processing time. It avoids the replacement of sensing equipment and also provides data quality assurance for subsequent sensing streaming media content analysis. It has certain research significance for the development of monitoring system with artificial intelligence management for target perception and tracking....
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