Developing a Video Buffer Framework for Video Streaming in Cellular Networks

Abstract

This work proposes a new video buffer framework (VBF) to acquire a favorable quality of experience (QoE) for video streaming in cellular networks. The proposed framework consists of three main parts: client selection algorithm, categorization method, and distribution mechanism. The client selection algorithm was named independent client selection algorithm (ICSA), which is proposed to select the best clients who have less interfering effects on video quality and recognize the clients’ urgency based on buffer occupancy level. In the categorization method, each frame in the video buffer is given a specific number for better estimation of the playout outage probability, so it can efficiently handle so many frames from different videos at different bitrates. Meanwhile, at the proposed distribution mechanism, a predetermined threshold value is selected for lower and upper levels of playout outage probability. Then, the control unit at the base station will distribute the radio resources and decide the minimum rate requirement based on clients’ urgency categories. Simulation results showed that the VBF grantees fairness of resources distribution among different clients within the same cellular network while minimizing the interruption duration and controlling the video buffer at an acceptable level. Also, the results showed that the system throughput of the proposed framework outperforms other existing algorithms such as playout buffer and discontinuous reception aware scheduling (PBDAS), maximum carrier-to-interface ratio (MAX-CIR), and proportional fair (PF) due to enhancing the quality of experience for video streaming by increasing the radio resources in fairness manner.

Proposed emerged and enhanced routing protocols for wireless networks

“…Before presenting the experimental results, we first introduced the background details of network assumptions, network constraints and performance parameters. Below are the hypotheses constraints are considered to do our experimental results for the proposed routing protocols [24], [25]: − All hosts contain perfect communication coverage opportunities assuming they are existed in a 2D space. − All hosts are located in same coverage ranges assuming the communication links are in full-duplex mode.…”

A new smart approach of an efficient energy consumption management by using a machine-learning technique

“…This work comes with some clear messages for us: the first approach includes descriptions with greater computational actual power, good covering for the differences in an increase during the day but with successfully accuracy, good fitting with energy expenditure, and with large overfitting for the variance in time (hours) at the day, but a validation approach provides the data that is used to track and evaluate the good performance of the electric consumers, to solve the problem of overfitting, and it is updating the training data more than once until the output data contains overfitting in the energy consumption less than or equal to 10%, the output power consumption is useful to consumers, the validation approach is completed, it means that the overfitting in the generalization process has been eliminated, and it is estimated with greater accuracy, better fitting for the variance in time (hours) at the day, therefore; we noted the 𝐶(𝐸𝐸) is decreased when the (A p ) is decreased then the (𝐸 𝑐 ) and the (𝐸𝐸 𝑐 ) are decrease also which is lead to make the energy consumption is easy to become efficient with know-how estimation a good use for the (𝐴 𝑝 ) to satisfy more efficient energy consumption. Finally, we present a case study of how energy consumption estimation may be used to educate the electrical model for solving its energy expenditure problem and to make the EECM processes have stronger precision energy consumption with lower cost by reducing the maximum rate of the (A p ) in the specific time (24) hours for one house. In the future, we could take this research in the many houses in the city, country for one, many months or years, and could increase public awareness of the importance of electrical energy to minimize the high energy consumption and learn the electric consumers how to use the energy in a true way to reach energy to all the city in a useful way without thieves of meter, hacking, and electricity theft.…”

“…To find a new method of our proposed model for electrical energy consumption to solve the exceeding energy consumption and theft problem, we used one of the famous machine learning (ML) methods, namely the validation approach [22]- [24]. The validation is an approach that was previously used by the AI community, but here in our research the validation approach has been formulated again with a radical change in the basics of its work with comprehensive change in the contents and basics of its work, this approach is connecting with the internet of thing (IoT) smart technology over a Wi-Fi network to save the management's data in the Google Firebase Cloud to become smart treatment one approach for the An efficient energy consumption management (EECM) approaches.…”

A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification

“…Popular SML techniques used by academics include naive Bayes, logistic regression, random forest, J48, CART, Multilayer perception, and support vector machine (SVM). Tis study evaluates 305 publications on SML classifcation algorithms in its initial compilation using the systematic literature review (SLR) methodology [5,6]. Several neural network classifers are applied to the Twitter-collected dataset of people's faces.…”