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Analysis of wireless English multimedia communication based on spatial state model equation

Publié en ligne: 15 Jul 2022
Volume & Edition: AHEAD OF PRINT
Pages: -
Reçu: 17 Apr 2022
Accepté: 09 Jun 2022
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Introduction

Roesser state space model is a common multi-dimensional system model based on practical engineering problems. It uses the input of the system and the previous state associated with it to control the current state. The nature of this local calculation not only greatly simplifies the mathematical expression of the multi-dimensional system, but also facilitates the analysis and research of the system. At present, the research based on Roesser model has made some progress [1]. These studies mainly focus on how to design algorithms to make the order of the implementation matrix of the system lower, such as EOA (elementary operation approach) transformation method and P-matrix method [2]. Using lower order implementation matrix to describe the system has strong practical application value, which can not only make the system respond more quickly, but also make the implementation and application of Roesser model in some energy limited systems possible. For example, in wireless sensor networks, the limited energy of sensor nodes is an important problem that puzzles its practical application. The energy consumption of sensor networks is mainly concentrated in the mutual communication between nodes, and its communication energy consumption is 2–4 times of the calculated energy consumption [3]. If each node can process the collected information and then spread it, the whole sensor network will have a longer network lifetime. With the introduction of dynamic system theory into the field of Applied Linguistics, language development can be regarded as a dynamic system, which opens a new perspective for the construction of demand analysis model of language courses. From the perspective of dynamic system theory, based on state space model, language development demand system can be regarded as a dynamic system composed of initial state, target state, system state variables and their dynamic relationship with time. With the rapid development of multimedia technology, multimedia sources can produce a large amount of data. Without data compression, the transmission and storage of multimedia information are difficult to be practical [4]. Multimedia coding and compression can quickly transmit multimedia sources, carry out more services in parallel and reduce storage costs. It is an important research direction in the field of multimedia communication. Multimedia coding is a field that covers many prerequisite courses. Only by mastering the relevant knowledge of random process, information theory and communication principle can we understand and practice the knowledge of multimedia coding [5]. Aiming at this research problem, Jothi S and others try to adopt new theories and new methods to solve the problems in multi-dimensional wireless sensor networks. However, the application of these new theories and methods is not mature, and there are still various deficiencies in the conclusions. Although multidimensional wireless sensor networks have important application prospects in reality, there is no representative application system of multidimensional wireless sensor networks at present, except that some simple regular structure wireless sensor networks are deployed to underwater sensor networks [6]. Swain C and others proposed a new EOA method. The EOA transformation method simplifies the implementation of Roesser state space model into linear transformation operation and specific supplementary operation on multi-dimensional characteristic polynomial matrix. This method is simple in concept and easy to calculate, and can analyze the influence of coefficient correlation on the implementation matrix [7]. Umamaheswari S and others found that each supplementary operation in EOA transformation method can only reduce one order. Although the supplement efficiency can be improved by some decomposition, in fact, only a few transfer functions are suitable for this decomposition method [8].

Based on the current research, according to the characteristics of space multimedia communication environment and communication equipment, this paper proposes a wireless English synchronous QoS control algorithm with low complexity and easy implementation. Using RTCPSRRTP timestamp and NTP absolute timestamp, the wireless English RTP timestamp is mapped to the same absolute reference clock (sender system time) respectively, and the wireless English synchronization detection decision rule is established according to the synchronization QoS performance requirements between wireless English media. At the receiving end, the wireless English stream is the main media stream and the video stream is the slave media stream. The synchronization detection and judgment is implemented in real time before the video frame is played. The synchronization between audio and video media is realized by corresponding synchronization control of wireless English playback. Simulation results show that the synchronization algorithm effectively improves the synchronization performance between audio and video media in space multimedia communication system and meets the QoS performance requirements of audio and video synchronization in space communication.

Methods
Wireless English synchronization algorithm for spatial multimedia communication

In 1990, Perona and Malik proposed a nonlinear anisotropic diffusion equation, namely the P-M model, in order to maintain image edge information. Definitions are as follows:

RTP / RTCP protocol is a network transmission protocol specially designed for real-time media transmission by the wireless English transmission working group of Internet Engineering Task Force (ETF). A synchronization control algorithm between wireless English media based on RTP / RTCP is proposed. The algorithm needs to process each RTCPSR (sender reports) message. The algorithm has high complexity and needs the support of network time protocol (NTP). In addition, the synchronization control is carried out by dropping frames or pausing decoding before wireless English decoding at the receiver, which not only ignores the delay jitter introduced by wireless English decoding, but also affects the normal decoding and reconstruction of video to a certain extent. According to the characteristics of space multimedia communication environment and communication equipment, this paper proposes a wireless English synchronous QoS control algorithm with low complexity and easy implementation [9].

Algorithm overview

Using RTCPSRRTP timestamp and NTP absolute timestamp, the wireless English RTP timestamp is mapped to the same absolute reference clock (sender system time) respectively, and the wireless English synchronization detection decision rule is established according to the synchronization QoS performance requirements between wireless English media. Wireless English synchronization control system model at the receiver. At the receiving end, the wireless English stream is the main media stream, and the video stream is the slave media stream. The synchronization detection and judgment are implemented in real time before the video frame is played. The synchronization between audio and video media is realized through the corresponding synchronization control of wireless English playback [10].

Wireless English audio and video RTP timestamp mapping

The RTP timestamp corresponds to the sampling time of the first data byte of the payload in the RTP packet. However, wireless English audio and video streams are transmitted in different RTP session streams, so the time stamps in RTP packets in different RTP session streams cannot be directly compared. The reason is that the initial value of the time stamp in the RTP packet is generated randomly, so even if the wireless English audio and video stream is sampled at the same time, the initial time stamp value of the RTP packet is different; The growth of RTP timestamp is monotonic and linear, but its growth rate is related to the media sampling rate. Generally, the sampling rate of wireless English audio and video media is different, so the timestamp growth rate of RTP packets in wireless English audio and video streams is also different. The increment of RTP timestamp in adjacent RTP packets is equal to the number of payload samples in RTP packets. In order to solve the above problems, the RTP timestamp in the wireless English audio and video stream is mapped to a common absolute reference time [11]. Using the relationship between RTP timestamp and NTP absolute timestamp carried in RTCPSR package and RTP timestamp in RTP package, the NTP absolute reference time corresponding to RTP timestamp in wireless English audio and video RTP package is calculated. The receiver can directly compare the calculation results and carry out corresponding synchronous QoS control.

The following formula can be obtained: TI(J)=TI(1)+K=2JΔTI(K)R {T_I}\left(J \right) = {T_I}\left(1 \right) + \sum\limits_{K = 2}^J {{{\Delta {T_I}\left(K \right)} \over R}}

Where, R is the RTP timestamp clock frequency, that is, the media data sampling rate. ΔTI(K) is the difference between RTP timestamps of two adjacent RTP packets: ΔTi(K)=Ti(K)Ti(K1) \Delta {T_i}\left(K \right) = {T_i}\left(K \right) - {T_i}\left({K - 1} \right)

TI(K) can be calculated by the following formula: TI(1)=TI(0)+Ti(1)Ti(0)R {T_I}\left(1 \right) = {T_I}\left(0 \right) + {{{T_i}\left(1 \right) - {T_i}\left(0 \right)} \over R}

Substituting equations (2) and (3) into equation (1), we get: Ti(J)=Ti(0)+ti(J)Ti(0)R {T_i}\left(J \right) = {T_i}\left(0 \right) + {{{t_i}\left(J \right) - {T_i}\left(0 \right)} \over R}

In the communication process after a call connection is established, the type of wireless English audio and video encoder will not change, so the clock frequency of wireless English audio and video media timestamp will not change, that is, R can be considered as a constant value. The RTP timestamp and NTP timestamp carried by the 1st and i-th RTCPSR packets can be obtained: R=tI(0)t1(0)TI(0)T1(0) R = {{{t_I}\left(0 \right) - {t_1}\left(0 \right)} \over {{T_I}\left(0 \right) - {T_1}\left(0 \right)}}

The NTP absolute timestamp corresponding to the media package can be obtained from equations (4) and (5): Ti(J)=T1(0)+ti(J)T1(0)R {T_i}\left(J \right) = {T_1}\left(0 \right) + {{{t_i}\left(J \right) - {T_1}\left(0 \right)} \over R}

RFC3550 specification does not require that NTP absolute time in RTCP must be generated by NTP time server, and its specific implementation has exceeded the content of RTP protocol specification. The sender can directly generate the NTP absolute time through any reference clock. Because the implementation of NTP protocol is complex, the system clock of the sender can be used to generate the NTP absolute timestamp value in RTCPSR packet. Through equation (6), the RTP timestamp of audio and video packets is mapped to the same absolute reference clock, that is, the system time of the sender. Therefore, the algorithm in this paper does not need the support of the whole network synchronization clock or NTP protocol, and can directly use the system clock of the sender. It can be seen from equation (6) that the algorithm in this paper only needs to process the NTP absolute time in the first RTCPSR packet as the common clock reference of subsequent audio and video media packets [12].

Decision rules and synchronization control of audio and video synchronization detection

Assuming that the jV-th audio packet and the jV-th video packet arrive at the playback end at the same time (the relevant parameters of audio and video session stream are represented by labels a and V respectively), after the absolute time corresponding to wireless English audio packet and video frame is calculated by equation (6). By comparing the wireless English audio and video time offset skew with the synchronization boundary threshold, the synchronization error is detected and judged, and the corresponding synchronization control is carried out according to the judgment results. Because the human ear is much more sensitive to audio playback than the human eye to video playback, and generally, the impact of discarding or delaying a small number of video frames on the subjective performance quality of video can be subjectively tolerated or even undetected by users [13]. In view of this, the wireless English audio stream is the main media stream, and the video stream is the slave media stream, that is, based on the playback of the audio stream, maintain the normal playback of the audio stream, make synchronous detection and judgment before the playback of the video frame, and control the playback of the video frame to achieve the synchronization between the audio and video media. The synchronization detection decision of the receiving end and the corresponding synchronization control method are as follows:

Case 1: Skew < T, at this time, the wireless English audio and video are not synchronized, and the video lags behind the audio. Skip (discard) the video frame that arrives late and will not be played;

Case 2: TSkewT+, at this time, the wireless English audio and video is in the synchronization area. Play the video frame directly without synchronous control;

Case 3: Skew > T+, at this time, the wireless English audio and video are not synchronized, and the video is ahead of the audio. Delay the playback of the early video frame, delay skew (MS) time, and then conduct synchronous detection and judgment.

Among them, the time offset of wireless English audio and video is Skew=TiVV(jV)TiAA(jA) Skew = T_{iV}^V\left({{j_V}} \right) - T_{iA}^A\left({{j_A}} \right) , and T = −80ms, T+ = +80ms is taken according to the synchronization Q8 performance parameters between audio and video media defined above.

Analysis of algorithm performance and characteristics

It can be seen from equation (6) that the wireless English audio and video synchronization algorithm proposed in this paper only needs to process one RTCPSR packet, while the traditional media synchronization algorithm based on RTP / RTCP needs to process multiple RTCPSR packets periodically (such as every 4 / 6 seconds) to continuously update the time stamps in the SR packets used in its algorithm, and its algorithm complexity is directly proportional to the number of SR packets processed. At the same time, this algorithm is easier to implement. Therefore, compared with the traditional media synchronization algorithm based on RTP / RTCP, this algorithm has lower algorithm complexity. In addition, the wireless English audio and video synchronization algorithm proposed in this paper also has the following characteristics:

Without the support of the whole network synchronization clock or NTP protocol, the system clock of the sender can be directly used. It is not affected by packet loss and can be applied to the environment with high packet loss rate of space communication. Media synchronization technology based on RTP / RTCP protocol is a relative timestamp synchronization technology based on time axis synchronization description model. Wireless English audio and video media objects are independently associated with a common time axis. The loss of any RTP packet will not affect the synchronization control of other media packets. If the first RTCPSR packet is lost, the next SR packet can be received as the first RTCPSR packet. Without feedback mechanism, it can be applied to the space communication network environment with long time delay [14].

State space model

System model is a mathematical model that reflects the causal relationship and transformation relationship between system variables. The commonly used mathematical models of discrete systems mainly include difference equation, transfer function model and state space model. Difference equation is a common mathematical model in one-dimensional system, but this model is not suitable for multi-dimensional system. The more complex the multidimensional system is, the higher the dimension of the system is. The higher the order of the difference equation (Discrete System) or differential equation (continuous time or space system) describing the system by the relationship between input and output is, the more difficult the system analysis is. Transfer function is the main mathematical model describing the system characteristics in classical control theory. It is suitable for the linear constant system of single input and single output (SISO). It can easily deal with the transient response analysis or frequency method analysis and design of this kind of system. However, this model is not suitable for multi input and multi output (MIMO) systems, time-varying systems and nonlinear systems. In addition, the transfer function is only an incomplete description of the system. It can only reflect the linear dynamic characteristics of the transfer between the input and output of the system, but can not reflect the dynamic change characteristics within the system, which greatly limits the application of the transfer function model in practice. The state space model is based on the analysis of the internal structure of the system, which is composed of the state equation describing the dynamic characteristics of the system and the output equation describing the transformation relationship between the system output variables and the state variables. It can reflect the changes of all independent variables in the multi-dimensional system, and then determine all internal motion states of the system [15]. Therefore, the state space model reflects all the information of the system dynamic behavior and is a complete description of the system behavior. The state space model has a wide range of applications. It is not only suitable for SISO linear constant systems, but also suitable for nonlinear systems, time-varying systems, MIMO systems and stochastic systems. For different systems, the mathematical expression is simple and unified. The more outstanding advantage is that the state space model adopts local calculation and uses the state variables inside the system to describe its dynamic characteristics. This property of local calculation not only greatly simplifies the mathematical expression of multi-dimensional system, but also facilitates the analysis and research of the system.

At present, the most representative state space models are Roesser model and Fornasini Marchesini model. Multidimensional Roesser state space model is based on the multidimensional system corresponding to the one-dimensional state space model of discrete-time system. It is a model established after the evolution of multidimensional system. The current input and the previous state of the system are associated with this control. Roesser state space model of one-dimensional discrete system is: xX(i+1)=Ax(i)+Bu(i) xX\left({i + 1} \right) = Ax\left(i \right) + Bu\left(i \right) y(i)=Cx(i)+DU(I) y\left(i \right) = Cx\left(i \right) + DU\left(I \right)

Where, u and y represent the input and output of the system respectively, and A, B, C and D are coefficient matrices. Formula and formula are the state equation describing the dynamic characteristics of the system and the output equation describing the transformation relationship between the system output variables and state variables. The two-dimensional Roesser state space model was first proposed by Roesser when he studied multi-dimensional linear filter networks in the 1970s. The Roesser state space model of single input and single output of two-dimensional linear discrete system is: [xh(i+1,j)xv(i,j+1)]=A[Xh(I,J)Xv(I,J)]+BU(i,j) \left[{\matrix{{{x^h}\left({i + 1,j} \right)} \hfill \cr {{x^v}\left({i,j + 1} \right)} \hfill \cr}} \right] = A\left[{\matrix{{{X^h}\left({I,J} \right)} \hfill \cr {{X^v}\left({I,J} \right)} \hfill \cr}} \right] + BU\left({i,j} \right) y(i,j)=C[Xh(I,J)Xv(I,J)]+DU(i,j) y\left({i,j} \right) = C\left[{\matrix{{{X^h}\left({I,J} \right)} \hfill \cr {{X^v}\left({I,J} \right)} \hfill \cr}} \right] + DU\left({i,j} \right)

Where, xh (i, j) ∈ Rr1 and xv (i, j) ∈ Rr2 represent the state vectors in the horizontal and vertical directions respectively; u(i, j) and y(i, j) represent input signal and output signal respectively; A, B, C, D are real matrices, and ARr×r, BRr×1, CRr, DR1×1, r = r1 + r2.

Roesser state space model of SISO

Extending the two-dimensional Roesser model in the horizontal and vertical directions to the multi-dimensional system, the Roesser state space model with multi-dimensional single input and single output is obtained as follows: [X1(i1+1,,in)xn(i1,,in+1)]=A[x1(i1,,in)xn(i1,,in)]+BU(i1,,in) \left[{\matrix{{{X_1}\left({{i_1} + 1, \ldots,{i_n}} \right)} \hfill \cr \ldots \hfill \cr {{x_n}\left({{i_1}, \ldots,{i_n} + 1} \right)} \hfill \cr}} \right] = A\left[{\matrix{{{x_1}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr \ldots \hfill \cr {{x_n}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr}} \right] + BU\left({{i_1}, \ldots,{i_n}} \right) y(i1,,in)=c[X1(i1,,in)xn(i1,,in)]+DU(i1,,in) y\left({{i_1}, \ldots,{i_n}} \right) = c\left[{\matrix{{{X_1}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr \ldots \hfill \cr {{x_n}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr}} \right] + DU\left({{i_1}, \ldots,{i_n}} \right)

Where, xk(i1,…,in) ∈ Rrk, k = 1,…, n represents the k-th sub state vector; DU(i1,…,in) and y(i1,…,in) represent input signal and output signal respectively; A, B, C and D are real matrices with sizes of r×r, r×1, 1×r, 1×1 respectively, and r=k=1nrk r = \sum\limits_{k = 1}^n {{r_k}} . The local state x(i1,…,in) is composed of sub state vectors, which are recorded as: X(i1,,in)=[x1(i1,,in)xn(i1,,in)] X\left({{i_1}, \ldots,{i_n}} \right) = \left[{\matrix{{{x_1}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr \ldots \hfill \cr {{x_n}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr}} \right]

The number of rows of local state X(i1,…,in) (equal to the order of matrix A) is called the order of n-dimensional Roesser model, and the dimension rk of sub state vector x(i1,…,in) is called pair variable zk, local order of k=l,…, n. The system is also simplified as a system (A, B, C, D). The transfer function corresponding to the system is: G(Z1,,Zn)=CZ(IrAZ)1B+D G\left({{Z_1}, \ldots,{Z_n}} \right) = CZ{\left({{I_r} - AZ} \right)^{- 1}}B + D

Where, Z = diag{z1 Ir1,…,zn Irn}, z1,…,zn represents unit delay operation. When the unit forward transfer operation si,si=zi1 {s_i},{s_i} = z_i^{- 1} , i = 1,…,n is adopted, the corresponding transfer function of the system is: G¯(s1,,sn)=G(S11,,Sn1)=C(SA)1B+D \bar G\left({{s_1}, \ldots,{s_n}} \right) = G\left({S_1^{- 1}, \ldots,S_n^{- 1}} \right) = C{\left({S - A} \right)^{- 1}}\,B + D

Where S = diag{s1 zn Ir1,…,sn Irn}. Block diagonal matrices S and Z are also usually represented by direct sum. For example, the matrix S can be expressed as S = s1 Ir1 ⊕ ⋯ ⊕ sn Irn.

For n-dimensional transfer function: G(Z1,,Zn)=n(z1,,zn)d(z1,,zn) G\left({{Z_1}, \ldots,{Z_n}} \right) = {{n\left({{z_1}, \ldots,{z_n}} \right)} \over {d\left({{z_1}, \ldots,{z_n}} \right)}}

Or G˜(S1,,Sn)=n˜(z1,,zn)d˜(z1,,zn) \tilde G\left({{S_1}, \ldots,{S_n}} \right) = {{\tilde n\left({{z_1}, \ldots,{z_n}} \right)} \over {\tilde d\left({{z_1}, \ldots,{z_n}} \right)}}

If matrices A, B, C and d make the formula or formula valid, then a, B, C and D are the Roesser model implementation matrix of the given transfer function.

Roesser state space model of MIMO

In the Roesser model with single input and single output, input u(i1,…,in) and output y(i1,…,in) are single signals. When the input and output become multiple signals, the Roesser model of single input and single output can be extended to the Roesser model of multiple input and multiple output. The Roesser state space model of multiple input and multiple output of linear discrete system with q-dimensional input and p-dimensional output is as follows: [X1(i1+1,,in)xn(i1,,in+1)]=A[X1(i1,,in)xn(i1,,in)]+BU(i1,,in) \left[{\matrix{{{X_1}\left({{i_1} + 1, \ldots,{i_n}} \right)} \hfill \cr \ldots \hfill \cr {{x_n}\left({{i_1}, \ldots,{i_n} + 1} \right)} \hfill \cr}} \right] = A\left[{\matrix{{{X_1}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr \ldots \hfill \cr {{x_n}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr}} \right] + BU\left({{i_1}, \ldots,{i_n}} \right) Y(i1,,in)=C[X1(i1,,in)xn(i1,,in)]+Du(i1,,in) Y\left({{i_1}, \ldots,{i_n}} \right) = C\left[{\matrix{{{X_1}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr \ldots \hfill \cr {{x_n}\left({{i_1}, \ldots,{i_n}} \right)} \hfill \cr}} \right] + Du\left({{i_1}, \ldots,{i_n}} \right)

Wherein, u(i1,…,in) ∈ Rq and y(i1,…,in) ∈ Rp represent input signals and output signals respectively; xk(i1,…,in) ∈ Rrk, k = 1,…,n) represents the k-th sub state vector; A. B, C and D are real matrices with sizes of r×r, r×q, p×r, p×q respectively, and r=k=1nrk r = \sum\limits_{k = 1}^n {{r_k}} . The transfer function is applicable to the single input single output system, while for the multi input multi output system, it corresponds to the transfer function matrix. The transfer function matrix of the system is: H(z1,,zn)=cz(IrAZ)1B+D H\left({{z_1}, \ldots,{z_n}} \right) = cz{\left({{I_r} - AZ} \right)^{- 1}}B + D

Where H(z1,…,zn) is a matrix of order p×q. In fact, each term in the transfer function matrix is a transfer function. For the rational transfer function matrix C, if each transfer function in the matrix is a (strict) causal transfer function, then this matrix is called a (strict) causal transfer function matrix. A multidimensional transfer function matrix exists in Roesser model if and only if it is a causal transfer function matrix. For a causal transfer function matrix H(z1,…,zn) of order p×q, the matrix right fraction description is expressed as: H(z1,,zn)=NR(Z1,,Zn)Dr1(Z1,,Zn) H\left({{z_1}, \ldots,{z_n}} \right) = {N_R}\left({{Z_1}, \ldots,{Z_n}} \right)D_r^{- 1}\left({{Z_1}, \ldots,{Z_n}} \right)

Where NR(Z1,…,Zn) and Dr(Z1,…,Zn) are n-dimensional polynomial matrices of p×q and q×q, respectively. If det Dr(0,…,0) ≡ / 0. H(z1,…,zn) is called causal transfer function matrix. If NR(0,…,0) = 0 and Dr(0,…,0) = Iq, H(z1,…,zn) is called a strict causal transfer function matrix.

Results and analysis

The air link rate of the wireless transceiver is 512Kbps. In the experiment, the video is encoded based on H.264 and the audio is encoded based on G.729 Speech coding standard. The audio and video parameters in the experiment are shown in Table 1.

Audio and video parameters

Encoder Bit rate Frame rate Format
Audio G 729A 8kbps 100fps 8KHz 16-bit
Video H.264 50kbps 15fps CIF(352×288)

The duration of each test is 5000 frames of video. The synchronization control algorithm between wireless English media based on RTP / RTCP and the synchronization algorithm proposed in this paper are used for audio and video synchronization. The RMSE results before and after audio and video synchronization are shown in Table 2, Figure 1 and Figure 2.

RMSE before and after synchronization

Before synchronization 96.26 78.22 176 141.7 90 49 74.12 79.02
Inter media synchronization control algorithm 40.19 37.06 40.01 39.05 41.09 37.64 39.09
Algorithm in this paper 38.9 36.49 39.25 38.98 38.97 37.17 38.83

Figure 1

Synchronization control algorithm between wireless English media after synchronization

Figure 2

After synchronization of wireless English synchronization QoS control algorithm

As shown in Table 2, compared with that before synchronization, the RMSE value after adopting the synchronization algorithm in this paper is reduced by 60.09% on average and 77.69% at the highest; Compared with the wireless English media synchronization control algorithm based on RTP / RTCP, the synchronization performance of this algorithm is better than the wireless English media synchronization control algorithm. The RMSE value is reduced by 2% on average and 5.2% at most. In addition, the synchronization control algorithm between wireless English media performs synchronization control by losing frames or pausing decoding before video decoding at the receiver, which affects the quality of wireless English video decoding and reconstruction.

Conclusion

This paper presents the analysis of wireless English multimedia communication based on spatial state model equation. The system model is a mathematical model that reflects the causal relationship and transformation relationship between system variables. The commonly used mathematical models of discrete systems mainly include difference equation, transfer function model and state space model. Difference equation is a common mathematical model in one-dimensional system, but this model is not suitable for multi-dimensional system. The more complex the multidimensional system is, the higher the dimension of the system is. The higher the order of the difference equation (Discrete System) or differential equation (continuous time or space system) describing the system by the relationship between input and output is, the more difficult the system analysis is. Space multimedia communication has the characteristics of high packet loss rate, long transmission time, large jitter and limited resources. In order to meet the QoS requirements of wireless English audio and video synchronization, a wireless English audio and video real-time synchronization algorithm based on RTP / RTCP protocol is proposed. The audio stream is the main media stream, and the wireless English audio and video synchronization is realized by controlling video playback. The algorithm does not need the whole network synchronous clock and feedback mechanism, has low algorithm complexity, and is suitable for spatial multimedia communication. Simulation results show that the synchronization algorithm effectively improves the synchronization performance between audio and video media in space multimedia communication system and meets the QoS performance requirements of audio and video synchronization in space communication. The research of this paper still has many places worthy of further study. Theoretically, we need to deeply analyze and compare the implementation methods of various multidimensional systems, so as to propose a new method to implement Roesser state space model. The application of Roesser in wireless sensor networks needs to be improved.

Figure 1

Synchronization control algorithm between wireless English media after synchronization
Synchronization control algorithm between wireless English media after synchronization

Figure 2

After synchronization of wireless English synchronization QoS control algorithm
After synchronization of wireless English synchronization QoS control algorithm

Audio and video parameters

Encoder Bit rate Frame rate Format
Audio G 729A 8kbps 100fps 8KHz 16-bit
Video H.264 50kbps 15fps CIF(352×288)

RMSE before and after synchronization

Before synchronization 96.26 78.22 176 141.7 90 49 74.12 79.02
Inter media synchronization control algorithm 40.19 37.06 40.01 39.05 41.09 37.64 39.09
Algorithm in this paper 38.9 36.49 39.25 38.98 38.97 37.17 38.83

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