In order to provide real-time and reliable guarantee for the mobile multimedia services, we analyzed the delay, jitter, and distortion of the wireless transmission of multimedia streams based on the wavelet model. The method of video frame resolution analysis and wavelet scale space are combined to establish a wavelet multimedia mobile analysis model. In the wavelet scale space, the opportunistic sampling method is used to capture the video frame sequence. In the opportunistic wavelet domain, the opportunistic perception method is used to capture the moving sequence of multimedia.
In the process of wavelet transform, we use the opportunistic wavelet transform to analyze the video frame sequence. The analysis result is helpful to process the jitter dispersion of multimedia mobile transmission. Large delay jitter of the video frame of the static multimedia transmission severely reduces the quality of the multimedia broadcast. The video frame sequence of the dynamic multimedia transmission is easy to be disorderly and aggravate the jitter.
In the opportunistic wavelet multimedia mobile model, the opportunistic video frame sequence is reconstructed by the opportunistic wavelet linear transform. This sequence is performed by opportunistic recombination resolution. This can obtain the opportunistic mobile multimedia video frame wavelet scale space. In the scale space and opportunistic wavelet domain, the moving scale coefficients and the opportunistic wavelet weights are adjusted in real time. This can generate a new type of mobile multimedia video frame.
Symbol F denotes the multimedia original data video frame sequence. In order to establish the opportunistic wavelet modeling in multimedia communication, we first convert the F into G sequence, as shown in the formula (1).
$$ G={\displaystyle \sum_{j= m\left( i-1\right)+1}^{k i}\delta {F}_j}, i=1,2,\cdots \left[\frac{k}{j}\right] $$
(1)
Here, δ is used to describe the opportunistic weight of the original video sequence. k represents the length of the original video frame sequence. j represents the size of the video frame. i represents a linear video frame number. Symbol m represents linear video frame size.
The opportunistic wavelet model is based on opportunistic wavelet linear transformation, which is used to obtain the opportunistic wavelet function O
W
and the opportunistic scaling function S
W
, as shown in the formula (2).
$$ \left\{\begin{array}{l}{O}_W(x)=\frac{3{k}^{\delta}}{2\left\Vert G(x)\right\Vert \left( k- m\right)}\\ {}{S}_W(x)=\frac{3 G(x)\left\Vert {F}_k\right\Vert }{2\delta}\end{array}\right. $$
(2)
The opportunistic weight coefficient can be obtained by the formula (3).
$$ \delta =\left\{\begin{array}{l}\left\Vert k\right\Vert, 0\le j<\frac{m}{3}\\ {}\sqrt{k^2+{j}^2},\frac{m}{3}\le j<\frac{m}{2}\\ {}-\left\lceil \frac{m}{k}\right\rceil, \begin{array}{ccc}\hfill j<0\hfill & \hfill or\hfill & \hfill j\ge \frac{m}{2}\hfill \end{array}\end{array}\right. $$
(3)
Here, P
a, b
represents the opportunistic wavelet video frame sequence between the multimedia mobile parameter a and the multimedia sequence scrambling weight b. Q
a, b
represents the spatial domain transfer functions with the opportunistic wavelet, as shown in the formula (4).
$$ \left\{\begin{array}{l}{P}_{a, b}={\displaystyle \underset{i=1}{\overset{k}{\int }} G{(i)}^{\delta} di}\\ {}{Q}_{a, b}={\displaystyle \prod_{t\to k}{F}_t{\displaystyle \underset{i=1}{\overset{k}{\int }}\delta G(i) di}}\end{array}\right. $$
(4)
