RETRACTED

This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:

(1)Discrepancies in scope(2)Discrepancies in the description of the research reported(3)Discrepancies between the availability of data and the research described(4)Inappropriate citations(5)Incoherent, meaningless and/or irrelevant content included in the article(6)Manipulated or compromised peer review

The presence of these indicators undermines our confidence in the integrity of the article’s content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process.

Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity.

We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation.

The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

Complexity
Volume 2021 (2021), Article ID 3449338, 10 pages
https://doi.org/10.1155/2021/3449338

Research Article

Optimization of Embedded Mobile Teaching Model Based on Network Streaming Media Technology

Jing Tian

Chongqing Youth Vocational & Technical College, Chongqing 400712, China

Correspondence should be addressed to Jing Tian; 20130859@cqnu.edu.cn

Received 9 April 2021; Revised 18 May 2021; Accepted 1 June 2021; Published 9 June 2021

Academic Editor: Zhihan Lv

Copyright © 2021 Jing Tian. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Integrating network technology fully into traditional teaching can realize resource sharing to the greatest extent, so the establishment of distance education and network teaching platform has become the inevitable development of the time. The purpose of this paper is to build an embedded mobile teaching model based on network streaming media technology. The technology application, system composition and structure, realization process, and teaching method of the system are introduced in detail. The system energy consumption, bit rate of video information, and buffer technology were optimized, respectively. In this system, the energy consumption optimization method of mobile streaming media is adopted, and Central Processing Unit (CPU) resources are allocated reasonably according to the principle of maximizing rewards, so as to achieve the purpose of reducing power consumption. The results show that the system can effectively ensure the normal transmission of large multimedia information stream data through the network by using streaming media playback technology, and users can control the teaching process through interactive operation, which makes the network multimedia distance teaching based on streaming media develop in an all-round way and bring advanced teaching mode for education.

1. Introduction

With the rapid development of computer network technology, communication technology, and multimedia technology, audio and video technology has been widely used in all walks of life and gradually infiltrated into people’s daily life. For teaching, remote education and the establishment of the network teaching platform are inevitable in the development of era, will be fully integrated into the network technology, and can not only well transfer knowledge, but also increase the flexibility and efficiency of knowledge dissemination. For the traditional teaching method, it is a new change where teaching and management will be fully integrated, which will help to promote the development of the whole teaching. Network teaching refers to the educational and teaching activities organized and carried out for the completion of specific learning tasks based on the Internet, guided by modern educational thoughts and theories, and taking hypermedia as the main form of expression [1]. Its biggest advantage is the realization of resource sharing, and the way of knowledge transmission is not affected by time, region, the number of audiences and other factors, so it has been widely noted by the education workers of Guangzhou University. Compared with the traditional teaching mode of “teachers, schools and textbooks as the center,” the network teaching based on the information platform weakens the dominant position of teachers and enriches the teaching contents and forms of presentation. From the perspective of network teaching, the following five characteristics are analyzed [2]: (1) there are many types of platforms, abundant resources, serious fragmentation, and systematic deficiency; (2) there is sharing quality resources and convenient and intuitive operation; (3) it is difficult to communicate nonverbal information through breaking the limit of the number of people in time and space; (4) the subject status of students is prominent and the free space is large; (5) there are comprehensive diagnosis points and objective diagnostic criteria in class. The integration of multimedia classroom teaching resources and information technology has greatly changed the traditional classroom teaching form, and this change will continue to happen in a certain period of time. At the present stage, the network education and the traditional education permeate each other, learn from each other, foster strengths, and avoid weaknesses. The integration of development should be an effective way to improve the quality of education and teaching. The modern distance network education in China is a new type of distance network education mode which is formed by using computer cooperation technology, multimedia information processing technology, communication technology, and streaming media technology. Multimedia teaching breaks the monotony of class. In the teaching process, modern teaching media should be reasonably selected and used. Through the novel image display method and the organic combination with traditional teaching means, various media information can be presented to the classroom, so as to form a scientific and reasonable teaching process and optimize the teaching effect.

The purpose of this paper is to construct and optimize the embedded mobile teaching model based on network streaming media technology. Network multimedia distance learning is to broadcast multimedia message stream through the network. One of the main broadcasting methods is streaming media technology. Streaming media playback technology means that the entire multimedia file is not downloaded before playback, but only the initial part of the capacity is stored in the buffer, so that the computer can play the data packets received [3]. Streaming download, while “output” mode of transport, not only makes the network multimedia remote teaching and dynamic delay significantly shortened but also greatly reduces the demand for system cache. Besides, it can also be synchronized, with identification and control, such as operation. All these advantages enable the streaming media technology to well apply in network multimedia remote teaching. The technology of streaming media is the most rapid development in recent years. The adoption of streaming media technology will promote the development of network multimedia distance teaching. The technology of streaming media can effectively ensure the normal transmission of large multimedia information stream data on the network, and users can control the teaching process through interactive operation. With the popularization of optical fiber broadband, the improvement of hardware performance, and the perfection of software function, the network multimedia distance teaching based on streaming media has developed in an all-round way, which will bring advanced teaching mode to education.

The rest of this paper is organized as follows: the related work is discussed in Section 2. Section 3 expounds the embedded mobile teaching model. Section 4 optimizes the system based on the constructed mobile teaching model. Section 5 summarizes the whole paper.

2. Related Work

In recent years, with the expansion of the application scope of wireless network, all circles of society gradually realize the importance of network course for teaching work, so the related research of mobile teaching model has become the focus of many scholars.

Using the real-time communication technology of Web Radiotelephone Communication (RTC) and the features of Hypertext Markup Language 5 (HTML5) without installing plug-ins, Shu et al. [4] proposed a new multimedia real-time online distributed mobile streaming media teaching mode based on smart mobile terminals for students, so as to realize mobile teaching, open teaching contents, and maximize utilization of shared courses and famous teachers’ resources. In order to better adapt to the reform and innovation of hybrid teaching mode, Jian [5] expounded the key points of the development of live broadcasting system from four aspects of face recognition technology, offline transcoding slice technology, and the application of live broadcasting system in training base and nonacademic education and training through the analysis of streaming media technology and the construction of live broadcasting system framework. The application of this technology can carry out online teaching through the way of online broadcast and realize the remote video broadcast teaching system which integrates the functions of teaching live broadcast and teaching interaction. Zorzi et al. [6] analyzed and discussed the design and implementation of the streaming media network teaching platform which is widely used in the current Flash Video (FLV) technology and proposed the general process of constructing the streaming media network teaching platform based on FLV technology. Using Real-Time Messaging Protocol (RTMP) streaming media technology, Su et al. [7] studied and realized a network multimedia live broadcast system based on NGINX. In addition to using computers or mobile phone terminals for live broadcasting and watching, this live broadcasting system can also use multiple broadcast rooms for simultaneous live broadcasting of different courses. This research solves the limitation of laboratory space and provides a strong technical support for the information and network of experimental teaching. The Peer-to-Peer (P2P) video platform architecture is structured, remote teaching system platform is based on P2P streaming video on demand function, and remote network education system based on streaming media technology P2P model is adopted to accomplish each node to the Internet video storage and effective conversion play point, making the teaching video streaming transmission effective and making the remote education enhance the quality and better meet the demand of modern remote network continuation to education. Ye et al. [8] introduce the composition and application of distance education based on streaming media technology of Windows Media Service. Therefore, the rapid development and popularization of the Internet have provided a strong market power for the development of streaming media business, and the application of streaming media business in teaching is becoming increasingly popular. Rapid knowledge dissemination and timely information update have become a very important part of people’s life.

3. Mobile Teaching Model Construction

3.1. System Framework

Traditional streaming media players are mainly streaming media players based on personal computer (PC). Although such streaming media players have strong playback functions and friendly user interface, the streaming media players based on CP platform cannot be separated from multimedia personal computer (PC). Imbedded mobile streaming media player effectively combines imbedded technology with mobile streaming media technology, making it have the advantages of small size, ease of carrying, high professional power, low cost, strong real-time power, and so on [9].

Mobile learning platform needs to provide a free, personalized, and rich learning space in the wireless network environment. Based on this platform, users can not be restricted by learning time and place, and freedom can be customized according to the actual situation of their learning. The mobile learning platform needs to realize the learning function within the coverage of the wireless network. The network multimedia distance teaching system based on streaming media adopts the client machine/server structure, and its main structure is shown in Figure 1.

Figure 1: Basic framework of mobile teaching platform based on streaming media.

The system is mainly composed of main control module, task scheduling module, negotiation and analysis module, network support module, and so on. This paper introduces the functions and principles of each module and mainly explains the lamp V synchronization machine system. This part of the user can be smooth, of high speed, and stable to enjoy the content of their own interest which has played an important role. Therefore, there is detailed introduction to take what strategy to ensure video and audio for the same step playback. The system adopts the two-level scheduling method of tasks, which takes the whole system as a task, and then calls subtasks in it, so as to synchronize, schedule, and perform other operations effectively and conveniently.

The network multimedia distance learning system based on streaming media includes the following four parts: (1) the system server which is a Web B server with specific functions, realizes the central control of the network multimedia distance learning system, issues the teaching information of the system, and is responsible for registering and verifying the identity of customers. According to the customer request and registration information, locate the streaming media server for the client and select the streaming media files. (2) Streaming media server: it is responsible for obtaining streaming media files from storage devices, establishing streaming media channels with client computers through network interfaces, and transmitting streaming media to client computers. (3) Client: it is an application program based on web browser, which makes media requests through video-on-demand (VOD) and receives multimedia information stream from streaming media server. At the same time, it can wind back, pause, and replay multimedia information stream. (4) Network system: in addition to the use of TCP/IP negotiation, it also uses Real-time Transport Protocol (RTP)/Real-time Transport Control Protocol (RTCP)/Real-Time Streaming Protocol (RTSP) streaming media transmission network protocol, to ensure users reliable bandwidth and to ensure high quality and smooth sound and picture real-time broadcast.

Among them, the embedded mobile streaming media playback system, as the core component of the system, uses the embedded Linux operating system’s multithread programming interface to achieve the application level construction of the entire playback system. The interface control thread is the main thread, and its process is shown in Figure 2. Its functions are to synchronize audio and video playback, accept and respond to user operations, and receive network transmission data. The network data parsing thread and audio and video decoding thread are two auxiliary threads; the former completes the reading of audio and video data, and the latter completes the decoding of media signals. The interface control thread encapsulates the RTSP protocol, realizes audio and video playback, pause, forward, backward, end, and other session control, reads the network data, and puts the RTP packet into the network data buffer, and the RTP packet is wrapped in the advanced video product (AVP) header, which starts with “$” and contains only one RTP packet. Network data parsing thread encapsulates the RTP protocol parsing and streaming media file; the former reads RTP packets from the network data buffer and implements RTP packets of ordering and parsing, removing the AVP headers. Each RTP package can contain only a part of the streaming media file package or several complete streaming media file packages. A complete streaming media file package is assembled or split, and the analysis function calls the streaming media file to parse the streaming media file package. The decoded audio and video files are put into the audio and video buffers.

Figure 2: The main loop of the interface control thread.

3.2. System Operation and Implementation

The user requests the multimedia information stream from the client to the server, and the streaming media is transmitted from the server to the client in the way of streaming transmission. On the client, the multimedia information stream can be played by the browser through the playback plug-in or streaming media player. After the user opens the web page of the multimedia teaching system server with WB explorer on the client, the operation process is as follows: (1) access a first-class media through the hyperlink on the web page; (2) after receiving the request, the streaming media server will send a signal to inform the client; (3) after the client receives the signal, switch to the player plug-in or streaming media player; (4) the player plug-in or streaming media player sends the received signal to the streaming media server; (5) the streaming media server starts to transmit multimedia information stream files in the way of streaming transmission, and the player plug-in or streaming media player starts to receive and play while receiving. The advantages of the system are short download time (the size of the multimedia file is irrelevant), convenient access, strong interaction, less resources, and saving a lot of local hard disk space.

The implementation of streaming transport requires an appropriate transport protocol. In the scheme of streaming transmission, Hyper Text Transfer Protocol (HTTP)/TCP is generally used to transmit control information, while RTP/User Datagram Protocol (UDP) is used to transmit real-time sound data. Streaming transmission process is as follows: after users select A first-class media services, between web browsers and web servers using HTTP/TCP exchange control information, so that the need of real-time data from the original information retrieval comes out, then the customer on the web is at device under A/V Helper procedures, using HTTP from A web server to retrieve phase locked parameters on Helper initialization program. The A/V Helper program and the A/V server run the Real-Time Flow Control Protocol (RTSP) to exchange control information required for A/V transmission.

There are two types of streaming: real-time streaming and sequential streaming [10]. Real-time streaming transmission refers to ensuring the bandwidth of the media signal and the matching of the network connection, so that the media can be viewed in real time. Real-time streaming is always real-time transmission, especially suitable for live events. It also supports random access. Users can fast forward or backward to view the content in front or behind. Sequential streaming transmission is a sequential download, in which users can watch online media while downloading files. At a given moment, users can only watch the downloaded part and cannot skip to the later part that has not been downloaded. It is a mode of on-demand transmission. Therefore, according to the streaming transmission mode, the network multimedia distance teaching mode based on streaming media can be divided into two kinds: synchronous broadcast mode and on-demand mode [11]. Synchronous broadcasting does not need to store streaming media files in advance, but digitizes the multimedia information stream and plays it directly. In the client side of the web browser or streaming media player, real-time play is a “teaching” based teaching method. The on-demand mode requires digitizing the multimedia information stream and storing it in a streaming media file format in the streaming media server. The client side controls the teaching process through interactive operation, which is a teaching mode based on “learning.” The two teaching methods have their own advantages and disadvantages, and the appropriate teaching methods can be selected according to different needs and specific situations.

3.3. Module Design

3.3.1. Main Control Module

The main control module is at the top of the whole streaming media system. It controls the entire system and provides the upper layer with the following playback controls: Start_connect, Select_rack, Start_stream, Play/Pause, Resume, and Stop. It responds to the play control command sent by the upper-level application and transfers it to the lower-level task module to execute. This module is actually an encapsulation layer, which does not implement specific playback operation itself, but calls the interface in the task module to complete all the playback control power. The master control module obtains the required information by registering the call back function to the lower level.

In this paper, the learner-learning resource scoring matrix R is decomposed in the basic SVD model. On this basis, the implicit feedback information of users is appropriately selected to expand the model, so as to achieve a good recommendation effect. We write R as the product of two low-rank matrices P and Q, as shown in

In addition, the score prediction formula of the SVD model requiring bias is expressed as

where rui is the score prediction of resource i by learner u and f is a prominent function that maps the actual value to a certain interval. pu is user feature vector, qi is learning resource feature vector, and bui is defined as

Then the sum of the squares of the total error can be calculated and expressed in

The implicit feedback information is extracted by SVD++ algorithm, and its prediction scoring formula is shown in

where F(u) is the set of implicit feedback information of learners and yj is the feature vector of learners represented by specific implicit feedback information.

3.3.2. Task Scheduling Module

The task scheduler module is the core of the entire streaming media system. It implements a task scheduler inside the streamline routine and executes internal tasks in the way of cyclic tuning. There are three permanent tasks (protocol launch, data buffer, and network import) and five real-time tasks (open session, setup session, play session, pauses session, and closes session) to complete the entire operation process such as RTSP exchange, RTP receive, and RTCP send.

The entire streaming system runs in a thread within the real-time operating system. A task scheduler is activated from the thread’s human function, which adds three resident tasks to the task list and loops through them. When a command related to streaming media player has been sent from the client or server, start the corresponding real-time task and add it to the task list. Each task implements its own set of state machines and performs its own functions through protocol parsing module, network support module, and real-time embedded operation system.

3.3.3. Protocol Parsing Module

(1)SDP parsing module: this module is responsible for analyzing the information of a streaming media session and obtaining the basic attributes of a streaming media session, including media class type, lattice type, required transmission bandwidth, and broadcast time frame, for the use of the control system of RTSP. The client receives the message from the server to eliminate and analyze the message and obtains the session description information.(2)RSTP analysis module: this module is responsible for analyzing the received RSTP message into the line, obtaining the message body, and using it in flow control. The client receives the RTSP message from the server and parses it to obtain the control operation of the convection. In addition, the module defines a set of internal and external states, with a definite correspondence between the two. By determining the internal state, we can find out which step the analytic work has reached and then proceed to the corresponding processing.(3)RTP parsing module: this module is mainly to separate the header information and data information of RTP. The head information of RTP will be stored in the given identity document (ID) and Channel_ifon in which the channel is determined. The head information of RTP will be extracted from the information behind the RTP header and put into the given media node recording the session data which is determined. This will provide the base for the processing of payload later.(4)RTCP parsing module: RTCP is used to synchronize multiple media files when multiple media files are transmitted at the server and mobile phone terminals at the same time. The mobile terminal will parse the server’s scene recognition (SR), BYE and other information, package route reflector (RR), and also access point (AP) information to the server.

3.3.4. A/V Module

The causes of audio and video asynchronization in the system can be divided into two kinds: delay caused by data at the terminal and delay caused by network transmission. The delay caused by the terminal includes the time difference caused by the audio and visual frequency data and the encoding algorithm when the sending end is processing the sampling and encoding modules and the receiving end is processing the decoding and playing modules [12]. The network transmission delay is caused by many factors such as the real-time transmission bandwidth, transmission distance, and the processing speed of network nodes [13].

The timestamp of RTP packet is 32 bits. The timestamp reflects the sampling time of the first byte in RTP packet [14]. The unit of the time stamp is L/sampling frequency. At the beginning of each playback, the initial value of the R inverse timestamp is random, and the subsequent RTP packet timestamp is continuously added. The length of the RTP timestamp is 32 bits, the chip source may be very long, and the value of the timestamp may have a loop.

It is not possible to directly use RTP to stamp line A/V into the same step, because the sampling rate of audio and visual frequency is not the same, and the time stamp is inconsistent. And the initial value of the RTP timestamp is machine-dependent. Before A/V synchronization can be carried out, it is necessary to make the audio and video time stamps uniform in scale and comparable. Therefore, the RTP timestamp should be converted to an NPT timestamp for forward synchronization. Compared with the traditional single machine synchronization method, this system can be used for real-time multimedia communication and multipoint communication and does not need to change the data stream and does not need to attach the synchronization channel. It is a relatively simple and feasible synchronous method.

4. System Optimization Strategy

4.1. Bit Rate Optimization Strategy for Video Information

In the face of the system to upload data information needs, the average speed of 25 kbps General Packet Radio Service (GPRS) network downlink is sufficient for Flash products, but for video works, it is very reluctant. Therefore, it is necessary to reduce the bit rate as far as possible before the user can receive the amount of receiving quality, which becomes the premise for the operation of mobile streaming media business under the network. In order to ensure the effect of mobile streaming media business operation, we design and implement an effective video information rate optimization strategy, namely, from different types of original start streaming media material solid having characteristics, to choose the adaptive video coding parameters and cutting video bit rate, suitable for GPRS network bandwidth, in order to obtain good viewing effect:

(1)Use sound frequency optimal original rules to ensure that users get clear, smooth audio listening effect(2)Adopt H.264/Automatic Voltage Control (AVC) standard which is more efficient than MPEG-4(3)From the solid features of the original streaming media material, adjust the frame rate, IP factor, and other parameters adaptively(4)Make the final audio-visual frequency combination rate suitable for GPRS network bandwidth, so as to obtain smooth audio and good video viewing effect

In H.264 coding, it is allowed to set a series of parameters that have a significant impact on the bit rate, such as video format, frame rate, IP parameters, IP frame factor, video size, etc. [15]. In the actual work, we start from the complexity of the source data, optimize and adjust these parameters into the action state, and finally achieve the balance between the bit rate and quality and quantity, so as to obtain good broadcast and viewing effect.

In order to reduce the bit rate, the number of Data Communication Terminal (DCT) lines is quantized in H.264. The DC and low frequency areas that have great influence on the reconstruction of video frames are less discarded in a smaller step size. When the high frequency region has little influence on the reconstruction of video frame, the high frequency series with less significance are abandoned more with larger step size. The engineering names are I frame factor and P frame factor, respectively [16]. Undoubtedly, the abandonment of the high frequency component will inevitably affect the video frame quality of the local area, resulting in the area smoothing phenomenon similar to that of mosaic. Therefore, the conclusion of the question is to find a balance between the bit rate and quality.

In order to compare the loss caused by the discarding of high-frequency messages, we selected video programs with strong changes in face edges and large facial close-ups for testing. It is shown that the IP frame rate drops rapidly within the range of 14∼28 fan (thin line), but not much for visual effects (thick line). However, if the IP frame is continuously increased, the bit rate drop slows down and the visual effect deteriorates rapidly. It can be seen that the trade-off between bit rate and visual effect is that the IP frame factor is 28–35, as shown in Figure 3.

Figure 3: Relationship between I/P factor and visual quality.

4.2. Optimization of Energy Consumption of Mobile Streaming Media

Compared with the rapidly growing application demand of mobile terminals, the battery storage capacity of mobile devices has not been significantly improved, and the video codec complexity is high and the energy consumption is high, which makes the user experience difficult to meet. Therefore, the energy consumption of mobile streaming media video is a hot topic in the current research. At present, the research on energy consumption optimization of streaming media mainly adopts the establishment of workload prediction in the process of streaming media data transmission, the adjustment of data sending and receiving rate, and the application-level energy consumption management strategy to improve the service quality of streaming media and reduce energy consumption [17, 18]. Considering the different behavior habits of mobile phone users, the energy consumption problem of streaming media video in mobile terminal is combined with Markov decision process [19, 20]. CPU resources are allocated based on the maximum reward principle of Markov decision process (MDP) model in different states, so as to reduce the energy consumption in mobile terminals while maintaining the playback quality of streaming media video. Experimental results show that, compared with Linux heterocore multiprocessing strategy, the proposed streaming video energy consumption optimization method can reduce the energy consumption of the terminal by up to 21%.

In ACPI power management in embedded environment, there are four power states (G0/G1/G2/G3) visible to users. The relation conversion diagram of the four power supply states is shown in Figure 4.

Figure 4: Power state transition diagram.

G0 is composed of several states, of which only four are discussed in this article: C0, C1, C2, and C3. When the processor is running normally, it is in the state C0, and the power consumption of the processor from C1 to C3 gradually decreases, the time of being awakened becomes longer, and the power consumption generated by awakening becomes larger. The transition relationship of the four states is shown in Figure 4. From the perspective of energy saving, this method can effectively reduce the power consumption of the system by reasonably reducing the time that the processor is in the running state (C0) for the same task and lengthening the time that it stays in the sleeping state (C1/C2/C3).

This paper simulated 10 users with different mobile phone usage habits. Because each user’s network moment is in change, in order to cover most of the network types (3G, 4G, 5G, and WiFi), this article will use uncertain network state specific for 4 kinds of known network statuses to explore the optimal strategy of different network environment, described in more detail in Figure 5—the user mobile phone network state transition probability.

Figure 5: Probability of mutual transition of user’s mobile network state.

Figure 5 shows the mobile network state transition probability of user A. For example, the white column indicates that a user’s phone’s network status switches from WiFi to 4G 3% of the time, from WiFi to 3G 1% of the time, from WiFi to 5G 12% of the time, and from WiFi to WiFi 84% of the time.

Figure 6 shows the four state percentages in the Cortex-A15 kernel before and after optimization. By comparing Figures 5 and 6, it can be seen that the total percentage of the processor in sleep state (C1, C2, C3) during the client video playback using the MDP optimization strategy increased by 12.8%. By adjusting the configuration of the processor in different network states, the data acquisition speed of the terminal buffer is accelerated, so that the opening time of the data port is reduced, and the time of the processor in the sleep state is increased. Since the power consumption of the processor in the working state is greater than that in the sleeping state, the optimization method proposed in this paper can better save the energy consumption of the streaming media video terminal.

Figure 6: Percentage of the four processor states under the HMP and MDP optimization strategies.

Figure 7 shows the average video playback energy consumption after using two different optimization strategies in ten test environments. According to the energy consumption comparison, compared with the HMP optimization strategy based on real-time adjustment of system load, the optimization strategy proposed in this paper, VMDP, can save more energy while ensuring video playback quality. Among the ten groups of experiments, the third group has a lower optimization effect, saving 10% of energy, while the ninth group has the best optimization effect, saving 21% of energy. Because this article sets the reward MDP model function to the network status, group 9 test experiments in the 5G or WiFi state for a long time, reward value increase, choose efficient processor configuration in the data acquisition phase, fill the data buffer time the shortest, and stream media video processor in the process of terminal to play in the most time of sleep. Therefore, when the network state is good, the method proposed in this paper can save more energy, which can reduce the energy consumption of the mobile terminal while maintaining the video playback quality.

Figure 7: Distribution of average video playback energy consumption after using two different optimization strategies in ten test environments.

4.3. Optimization of Buffer Algorithm

In the field of traditional caching application, an obvious feature is that the cached data blocks have a high probability of being used due to the particularity of the application. For example, traditional streaming video, which is nearly an hour long, needs to be divided into multiple data blocks, and the caching system can quickly determine which data blocks after the viewing point should be cached based on the current viewing point. Because of the continuous nature of the video, these blocks of data have a high probability of being used, so the cache also has a certain hit rate guaranteed [21–23]. Different from the traditional field of streaming media, videos on the Internet are characterized by short time, miscellaneous categories, and large number, and there is no continuity and sequence between videos. So, in order to ensure that the cached content has a certain probability of being used, we need to integrate statistical data and conclude a reasonable model to judge the user’s preference for video. Recommendation algorithm is the solution under these scenarios.

To solve the above problems, this paper proposes using clustering signals to strengthen the recommendation method based on partial order signals and obtains a multisignal fusion video recommendation model. Take advantage of the recommendation algorithm, in the “smart phone oriented collaborative mobile streaming media service system,” build a caching mechanism, on the basis of the recommendation technology, guarantee the cache hit ratio through broadcast transmission to effectively reduce the transmission of video content in ad hoc network, and specific video cache mechanism based on clustering is recommended. With the popularity of social networks and users becoming interested in uploading their own videos, more and more videos on the web are shorter than 10 minutes, making traditional caching methods unsuitable. On the one hand, shorter video time makes caching more difficult. On the other hand, users are starting to learn to watch video in mobile environments, where caching is more important than in wired environments [24–26]. In order to do a short video cache, the first step is to accurately judge which users in the future will watch a short video (usually mobile storage space is large enough to hold several videos full or partial), so we recommend based algorithm to judge the user preference for video, high value of the video by caching preference, and ensuring the cache hit ratio. At the same time, in the “collaborative mobile streaming media system in intelligent mobile phone” establishment of a recommendation over the cache module, by using the broadcast feature of wireless transmission, the equipment can be interested in listening to the video, so as to achieve the effect of a transfer many times to meet, further reducing video traffic transmission in wireless ad hoc network, which relieves the wireless ad hoc network channel conflict.

Based on the above hypothesis, we conducted three groups of experiments, which, respectively, correspond to changing the cache size, changing the number of network users, and comparing the difference in effect of different recommendation algorithms. In the experiment, we extracted the top 50% of the viewing records of each user as the history records; the trained model was used to simulate the cache and the last 50% of the history on the timeline as the hit criteria. The experiment is to obtain the communication bandwidth saved in each case, and its calculation formula is

where hit_num represents all videos hit by all users’ cache, distinct-videos is the number of different videos recommended, and since the recommended videos are transmitted via broadcast, each video costs only one unit. User_num is the number of all users, times 20 because we are assuming that each user is going to watch 20 videos. Thus, communication_save represents the percentage of the message reduced to the original message.

Figure 8 shows the result graph of the transfer savings with the change in cache size for a network of 100, 500, and 1000 users. The general trend is that the larger the cache, the higher the percentage savings. When the number of users is small and the number of caches is small, caching technology increases the burden on the network, because the recommended video needs to be transmitted by broadcast, and the number of hit videos is not enough to offset the overhead of broadcast transmission.

Figure 8: The relationship between the transport savings and the number of buffers with different number of users.

5. Conclusion

In this paper, an embedded mobile teaching model based on network streaming media technology is constructed, and the system energy consumption, the bit rate of video information, and buffer technology are optimized. The streaming media technology adopted in this system can effectively ensure the normal transmission of large multimedia information stream data on the network, and users can control the teaching process through interactive operation. The video information bit rate optimization strategy can realize the adaptive selection of video encoding parameters from the inherent characteristics of different types of raw streaming media materials, so that the final audio and video bit rate is suitable for the GPRS network bandwidth so as to obtain good viewing effect. By integrating different users’ usage habits, video playback power consumption, playback time, and current network state, CPU resources are allocated reasonably to achieve the goal of reducing power consumption. With the popularization of optical fiber broadband, the improvement of hardware performance, and the perfection of software function, the network multimedia distance teaching based on streaming media has developed in an all-round way, which will bring advanced teaching mode to education.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The author declares no conflicts of interest in this paper.

References

T. Xia, H. Peng, and Y. B. Fan, “Research and implementation of embedded video surveillance streaming server system in mobile network,” Microprocessors, vol. 1, pp. 140–143, 2009.

A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, and R. Zimmermann, “A survey on bitrate adaptation schemes for streaming media over HTTP,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 562–585, 2018.

L. I. Yue, “Construction of network teaching system by using of real streaming media technology,” Journal of Liaoning Technical University, vol. 3, pp. 46–56, 2005.

L. Shu, Y. Zhang, Z. Yu, L. T. Yang, M. Hauswirth, and N. Xiong, “Context-aware cross-layer optimized video streaming in wireless multimedia sensor networks,” The Journal of Supercomputing, vol. 54, no. 1, pp. 94–121, 2010.

T. Jian, “Design and implementation of music teaching system based on streaming media,” Multimedia Systems, vol. 3, pp. 380–389, 2019.

M. Zorzi, A. Zanella, A. Testolin, M. De Filippo De Grazia, and M. Zorzi, “Cognition-based networks: a new perspective on network optimization using learning and distributed intelligence,” IEEE Access, vol. 3, pp. 1512–1530, 2015.

G.-M. Su, Z. Han, M. Wu, and K. J. R. Liu, “Joint uplink and downlink optimization for real-time multiuser video streaming over WLANs,” IEEE Journal of Selected Topics in Signal Processing, vol. 1, no. 2, pp. 280–294, 2007.

C. Ye, M. C. Gursoy, and S. Velipasalar, “Power control for wireless VBR video streaming: from optimization to reinforcement learning,” IEEE Transactions on Communications, vol. 67, no. 8, pp. 5629–5644, 2019.

A. Mukherjee, P. Deb, and D. De, “Natural computing in mobile network optimization,” Handbook of Research on Natural Computing for Optimization Problems, IGI Global, PA, USA, 2016.

S. Pasricha, S. Mohapatra, M. Luthra, N. Dutt, and N. Venkatasubramanian, “Co-optimization of streaming multimedia QOS and backlight power consumption for mobile handhold devices,” Korea Multimedia Society, vol. 7, no. 4, pp. 1–14, 2003.

A. Nakao, P. Du, Y. Kiriha et al., “End-to-end network slicing for 5G mobile networks,” Journal of Information Processing, vol. 25, pp. 153–163, 2017.

P. Rajaram and C. Gopalan, “An efficient coronary disease diagnosis system using dual-phase multi-objective optimization and embedded feature selection,” Coronary and Cardiothoracic Critical Care, IGI Global, PA, USA, 2019.

D. Parry, “Mobile perspectives: on teaching mobile literacy,” Educause Review, vol. 46, no. 2, pp. 14–16, 2011.

Y.-T. Sung, K.-E. Chang, and T.-C. Liu, “The effects of integrating mobile devices with teaching and learning on students’ learning performance: a meta-analysis and research synthesis,” Computers & Education, vol. 94, pp. 252–275, 2016.

M. M. Grant, S. Tamim, D. B. Brown, J. P. Sweeney, F. K. Ferguson, and L. B. Jones, “Teaching and learning with mobile computing devices: case study in K-12 classrooms,” TechTrends, vol. 59, no. 4, pp. 32–45, 2015.

N. Arulanand, A. R. Babu, and P. K. Rajesh, “Enriched learning experience using augmented reality framework in engineering education,” Procedia Computer Science, vol. 172, pp. 937–942, 2020.

Z. Nurbekova, V. Grinshkun, G. Aimicheva et al., “Project-based learning approach for teaching mobile application development using visualization technology,” International Journal of Emerging Technologies in Learning (iJET), vol. 15, no. 8, pp. 130–143, 2020.

G. Farias, E. Fabregas, E. Peralta, H. Vargas, S. Dormido-Canto, and S. Dormido, “Development of an easy-to-use multi-agent platform for teaching mobile robotics,” IEEE Access, vol. 7, pp. 55885–55897, 2019.

A. Basal, S. Yilmaz, A. Tanriverdi et al., “Effectiveness of mobile applications in vocabulary teaching,” Contemporary Educational Technology, vol. 7, no. 1, pp. 47–59, 2016.

Y. Mehdipour and H. Zerehkafi, “Mobile learning for education: benefits and challenges,” International Journal of Computational Engineering Research, vol. 3, no. 6, pp. 93–101, 2013.

J. Wen, J. Yang, B. Jiang, H. Song, and H. Wang, “Big data driven marine environment information forecasting: a time series prediction network,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 1, pp. 4–18, 2021.

J. Yang, J. Zhang, and H. Wang, “Urban traffic control in software defined Internet of things via a multi-agent deep reinforcement learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 6, pp. 3742–3754, 2021.

X. Liu, H. Song, and A. Liu, “Intelligent UAVs trajectory optimization from space-time for data collection in social networks,” IEEE Transactions on Network Science and Engineering, p. 1, 2020.

C. Chen, C. Wang, T. Qiu et al., “A robust active safety enhancement strategy with learning mechanism in vehicular networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 12, pp. 5160–5176, 2019.

Q. Du, H. Song, and X. Zhu, “Social-feature enabled communications among devices toward the smart IoT community,” IEEE Communications Magazine, vol. 57, no. 1, pp. 130–137, 2018.

S. N. Kersey, “Student perceptions on teaching and learning using open educational resources in college calculus,” Journal of Computers in Mathematics and Science Teaching, vol. 38, no. 3, pp. 249–265, 2019.