CN113473245B - A method for optimizing the latency of video streaming based on renewable energy - Google Patents

Abstract

The invention discloses a waiting time optimization method for UND video stream based on renewable energy, which comprises the steps of obtaining the downlink data rate of a video device connected to a corresponding millimeter wave base station at a time slot t and the data volume in a playback buffer of the video device at the beginning of the time slot t; according to the downlink data rate and the data volume, acquiring the video playing waiting time of the video equipment in the time slot t due to insufficient data volume; calculating battery electric quantity change data corresponding to the millimeter wave base station; according to the video playing waiting time of the video equipment, determining the total waiting time of all the video equipment in the coverage range of the millimeter wave base station corresponding to the time slot t; the battery electric quantity change data is used as a constraint condition of the total waiting time; and optimizing the total waiting time by adopting a Lyapunov optimization method to obtain a minimum value. By the method, the average waiting time of the video equipment of the user can be reduced while the network is kept stable, and the experience of the user in watching the video is provided.

Description

A method for optimizing the latency of video streaming based on renewable energy

technical field

The invention belongs to the field of UND video stream optimization, in particular to a method for optimizing the waiting time of UND video streams based on renewable energy.

Background technique

Currently, ultra-dense wireless communication networks (UDNs) provide a robust communication infrastructure for video applications and other data-intensive needs. However, with the continuous increase in the types of access devices and services in the Internet of Things, wireless communication systems will face increasing environmental pressures and economic costs. In order to solve this problem, in the prior art, renewable energy harvesting technology is introduced into the UDN system, so as to reduce traditional energy consumption and improve network service capacity. However, UDN small and medium base stations are often affected by changes in the surrounding environment in the process of collecting energy, so the energy collection is unstable, and sometimes the energy collected by the base station cannot meet its own working requirements. In addition, the channel state between the service user and the base station is complex, and the distribution and use of the collected renewable energy is very unreasonable, which greatly affects the system performance of the UDN. The above problems all cause users to wait for a long video buffering time when watching videos, which seriously affects the user's experience when watching videos.

Lyapunov optimization methods have been widely used in communication and queuing systems in recent years. This method realizes the system stability theory by decoupling the original multi-slot optimization into several sub-optimization problems, which can effectively reduce the computational complexity. In addition, the method only needs to collect current data in the optimization process without considering future data, thus avoiding the randomness problem caused by predicting and estimating channel gain. More importantly, when the number of system variables increases, the computational complexity of systems using traditional heuristic optimization algorithms or dynamic programming grows exponentially, while Lyapunov optimization only exhibits linear growth.

Therefore, how to effectively utilize the energy collected by the base station through the Lyapunov optimization method, minimize the waiting time of video playback, and improve the user's experience when watching videos; it has become a key issue of current research.

SUMMARY OF THE INVENTION

In view of the above problems, the present invention provides a method for optimizing the waiting time of a video stream based on renewable energy UND, which solves at least some of the above technical problems. Through this method, the average waiting time of the user's video equipment can be reduced while maintaining the network stability, and the user's video equipment can be improved. The experience of watching a video.

Embodiments of the present invention provide a method for optimizing the waiting time of video streams based on renewable energy sources, including:

S1. Obtain the downlink data rate at which the video device is connected to the corresponding millimeter-wave base station at time slot t, and the amount of data in the playback buffer of the video device at the beginning of time slot t;

S2, according to the downlink data rate and the data amount, obtain the video playback waiting time experienced by the video device in the time slot t due to insufficient data amount;

S3. Calculate the battery power change data of the corresponding millimeter-wave base station;

S4. Determine the total waiting time of all video devices within the coverage area of the corresponding millimeter-wave base station in time slot t according to the video playback waiting time of the video device; the battery power change data is used as a constraint condition for the total waiting time ;

S5. Using the Lyapunov optimization method, the total waiting time is optimized to obtain a minimum value.

Further, the S1 specifically includes:

S11. Suppose there are M millimeter-wave base stations in the network, denoted as BS _j ,j∈1,2,...,M; and each millimeter-wave base station has K _j video devices served by BS _j for playing video , where the i-th video device corresponding to BS _j is denoted as UE _ij ;

S12, all the frequency spectrums of BS _j are normalized to 1, and the frequency spectrum occupied by each video equipment and BS _j transmission corresponds to (N _j ) ^-1 ; the construction time slot t is connected to the downlink of the video equipment of BS _j The data rate formula is:

In formula (1), K _j is the number of all video devices within the coverage of the millimeter-wave base station BS _j , that is, the index number; P _j is the output power of the millimeter-wave base station; σ ² is the difference between the two millimeter-wave base stations. power level of thermal noise; L _ij (d _ij ) is the path loss of distance d _ij between the video equipment and its corresponding millimeter-wave base station; G _b and _Gu are the antenna gains of the millimeter-wave base station and the video equipment, respectively ;

S13: Acquire the amount of data in the playback buffer of the video device at the beginning of the time slot t.

Further, the S2 specifically includes:

When the data in the playback buffer of the video device is exhausted, the video device has an experienced video playback waiting time; the video playback waiting time is expressed as:

In formula (2), T ₀ is the length of a time slot; B _ij (t) is the amount of data in the playback buffer of the video device when time slot t begins; r _ij (t) is the amount of data in the video The rate of data consumption in the device's playback buffer.

Further, the S3 specifically includes:

S31. Assume that in time slot t, the energy obtained by the millimeter-wave base station from the renewable energy is e _j (t); the renewable energy obtained during the day has a maximum value e _max ; then

S32. Limit the battery power transferred between the millimeter-wave base stations in the t time slot:

In formula (5), P _j (t) is the transmit power; E _j (t) is the battery power at the current millimeter-wave base station; ε _jj′ (t) is the battery transferred from the current millimeter-wave base station to other millimeter-wave base stations power; ε _j′j (t) is the battery power transferred from other millimeter-wave base stations to the current millimeter-wave base station; n is the energy transmission efficiency, the n∈[0,1]; n×ε _j′j (t) is the total battery power received by the current millimeter-wave base station from other millimeter-wave base stations;

Also need to meet the conditions:

P _j (t)≥0,ε _j′j (t)≥0,j′≠j (6)

S33. Calculate the battery power change data of the corresponding millimeter-wave base station:

In formula (7), t+1 represents the next time in the t slot.

Further, the S4 specifically includes:

S41, calculate the total waiting time of all video devices within the coverage of the corresponding millimeter-wave base station according to formula (2):

S42, describe the multi-slot optimization problem through mathematical expressions, and extract the optimization constraints:

In formula (9), E[ ] is the average of the total waiting time of the video equipment; α is the set parameter α∈[0,1]; Constraint C1 limits the video playback waiting time; Constraint C2 ensures that the battery only needs a limited battery capacity; Constraint C3 limits the battery power obtained from renewable energy and other mmWave base stations to be greater than the battery power consumed by current mmWave base stations; Constraint C4 represents P _j (t) and ε _{j 'j} (t) is a non-negative number.

Further, the S5 specifically includes:

S51. Set a virtual queue:

Set two virtual queues F _ij (t) and E _j (t), where F _ij (t) is the virtual queue of the video playback waiting time, corresponding to the video playback waiting time of the video device, and E _j (t) is the battery power of the base station The virtual queue corresponding to the battery power of the base station, there are:

F _ij (t+1)=F _ij (t)+max{f _ij (t)-α,0} (13)

When the constraint C1 of the formula (9) is satisfied, it means that the rate of the virtual queue F _ij (t) is stable; when the constraint C2 of the formula (9) is satisfied, it means that the rate of the virtual queue E _j (t) is stable;

S52. Convert the multi-slot optimization problem into a sub-optimization problem under a single time slot:

The connection vector Z(t) of the virtual queue is defined as:

Z(t)=[F _ij (t), E _j (t)] (15)

Combining (15) with Lyapunov, we get:

According to (15) and (16) we get:

In equation (17), Δ(t) is an abbreviation for Δ(Q(t)); c ₁ and c ₂ are constants that satisfy the following constraints for all time slots t:

为某个时隙下从其它毫米波基站转移到当前毫米波基站的电池电量的最大值；

为某个时隙下从当前毫米波基站转移到其它毫米波基站的最大值；e_max为毫米波基站获取可再生能源的最大值；P_max为发射功率的最大值；In formula (19),

is the maximum battery power transferred from other millimeter-wave base stations to the current millimeter-wave base station in a certain time slot;

is the maximum value transferred from the current millimeter-wave base station to other millimeter-wave base stations in a certain time slot; e _max is the maximum value of renewable energy obtained by the millimeter-wave base station; P _max is the maximum value of transmit power;

According to Equation (15), Equation (17) can be converted into

In formula (20), V is the penalty weight, and the V≥0;

The corresponding bounds in Eq. (17) are relaxed, and the multi-slot optimization problem Eq. (9) is transformed into a sub-optimization problem under a single slot:

S54 , performing an optimal design on formula (21).

Further, the S54 specifically includes:

S541, judge the constraint condition of formula (21), and rewrite it as:

S542, the left side of the inequality of equation (22) is a convex function, and combined with the optimization function in equation (21) is a convex function, it is clear that the sub-optimization problem under each time slot is a convex optimization problem;

S543 , using a convex optimization tool to solve equation (21) to obtain the corresponding (P _j (t), ε _jj′ (t)), so as to realize the shortest corresponding waiting time.

Compared with those of the prior art, the method for optimizing the waiting time of a video stream based on renewable energy UND recorded in the present invention has the following advantages:

The Lyapunov optimization method is introduced in the present invention, which minimizes the average waiting time by decomposing the original multi-slot optimization problem into multiple single-slot sub-problems, effectively reduces the average waiting time of user video equipment while maintaining network stability, and ensures that It provides users with a stable and continuous viewing experience.

Description of drawings

The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the attached image:

FIG. 1 is a flowchart of steps of a method for optimizing the waiting time of a video stream based on renewable energy UND provided by an embodiment of the present invention.

FIG. 2 is a system model provided by an embodiment of the present invention.

FIG. 3 is an energy cooperation model between millimeter-wave base stations according to an embodiment of the present invention.

FIG. 4 is a discrete-time system model including K queues provided by an embodiment of the present invention.

FIG. 5 is a graph showing the relationship between the average energy queue length and V of the present invention.

FIG. 6 is a graph showing the relationship between the average waiting time and V of the BS ₁ of the present invention.

FIG. 7 is a graph showing the relationship between the average waiting time and V of the video equipment corresponding to the other five base stations of the present invention.

FIG. 8 is a relationship curve between the average waiting time and the time slot corresponding to the method for optimizing the waiting time of the video stream based on the renewable energy and the video stream according to the present invention.

FIG. 9 is a relationship curve between an average waiting time and a time slot using a greedy algorithm according to an embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be more thoroughly understood, and will fully convey the scope of the present disclosure to those skilled in the art.

Referring to FIG. 1 , an embodiment of the present invention provides a method for optimizing the waiting time of a video stream based on renewable energy UND, which specifically includes the following steps:

S1, obtain the downlink data rate at which the video device is connected to the corresponding millimeter-wave base station at time slot t, and the amount of data in the playback buffer of the video device at the beginning of time slot t;

S2. According to the downlink data rate and the data amount, obtain the video playback waiting time experienced by the video device due to insufficient data amount in the time slot t;

S3. Calculate the battery power change data corresponding to the millimeter wave base station;

S4, according to the video playback waiting time of the video equipment, determine the total waiting time of all the video equipment in the coverage area of the millimeter wave base station corresponding to the time slot t; the battery power change data is used as a constraint condition of the total waiting time;

S5. The Lyapunov optimization method is used to optimize the total waiting time to obtain the minimum value.

Through this method, the average waiting time of the user's video equipment can be effectively reduced while maintaining the network stability, and the user's experience when watching the video can be improved.

Each of the above steps will be described in detail below.

In the above steps S1 and S2, as shown in FIG. 2, a system model supporting downlink energy coordination is established. In the network, there are M millimeter-wave base stations, denoted as BS _j ,j∈1,2,...,M, and each millimeter-wave base station has K _j video devices represented by UE _ij served by BS _j It is used to play video, where the i-th video device corresponding to BS _j is denoted as UE _ij . Each mmWave base station is equipped with renewable energy harvesting equipment and is powered entirely by renewable energy. Each mmWave base station can share energy through the smart grid and transmit data to multiple mobile user video devices. The video device requests different video data from the server, which sends the requested video data to the mobile user over the shared wireless spectrum.

All spectrums of BS _j are normalized to 1, and the spectrum occupied by each video device and BS _j transmission corresponds to (N _j ) ^-1 ; based on the Shannon equation, construct the downlink of the video device connected to BS _j at time slot t The link data rate formula is:

In formula (1), K _j is the number of all video devices within the coverage of the millimeter-wave base station BS _j , that is, the index number; P _j is the output power of the millimeter-wave base station; σ ² is the thermal noise between the two millimeter-wave base stations power level; under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, data transmission has different path loss laws; line-of-sight conditions are used in this embodiment, and channel fading obeys large-scale line-of-sight-based path fading , _{Li ij} (d _ij ) is the path loss of distance d _ij between the video equipment and its corresponding millimeter-wave base station; all millimeter-wave base stations and video equipment in the system model are equipped with directional antennas, and G _b and _Gu are respectively Antenna gain of mmWave base stations and video equipment;

When the data in the playback buffer of the video device is exhausted, the video device has the video playback waiting time experienced; the video playback waiting time is expressed as:

(2) in the formula, the unit of the video playback waiting time is seconds; T ₀ is the length of a time slot; B _ij (t) is the amount of data in the playback buffer of the video equipment when the time slot t begins, and the unit is a bit; r _ij (t) is the data consumption rate in the playback buffer of the video device when the video is playing.

According to formula (2), the amount of data B _ij (t) in the playback buffer of the video device at time slot t+1 can be obtained as:

B _ij (t+1)=B _ij (t)+R _ij (t)−(1−f _ij (t))r _ij (t) (3).

In the above step S3, as shown in FIG. 3, an energy cooperation model between millimeter-wave base stations is established. Assuming that in time slot t, the energy obtained by the millimeter-wave base station from renewable energy is e _j (t); the renewable energy obtained during the day has a maximum value e _max ; then

Since the total energy consumed by each millimeter-wave base station cannot exceed the total energy including the collected energy and the transferred energy, the battery power transferred between the millimeter-wave base stations needs to be limited within the t time slot:

In equation (5), set the size of one time slot to be 1s, then the transmit power of BS _j at time t is P _j (t) × (one time slot). For the convenience of description, the transmit power is expressed as P _j (t); E _j (t) is the battery power at the current mmWave base station; ε _jj′ (t) is the battery power transferred from the current mmWave base station to other mmWave base stations; ε _j′j (t) is the battery power transferred from the current mmWave base station to other mmWave base stations The battery power transferred from other millimeter-wave base stations to the current millimeter-wave base station; n is the energy transmission efficiency, n∈[0,1]; n×ε _j′j (t) is the current millimeter-wave base station received from other millimeter-wave base stations total battery power;

Also need to meet the conditions:

P _j (t)≥0,ε _j′j (t)≥0,j′≠j (6)

Calculate the battery power change data of the corresponding millimeter-wave base station according to formula (5):

In formula (7), t+1 represents the next time in the t slot.

Calculate the total waiting time of all video devices within the coverage of the millimeter-wave base station according to formula (2):

The multi-slot optimization problem is described by mathematical expressions, and the optimization constraints are extracted:

In formula (9), E[ ] is the average of the total waiting time of the video equipment; α is the setting parameter α∈[0,1]; the constraint C1 limits the video playback waiting time; Requires limited battery capacity; Constraint C3 limits the battery power obtained from renewable energy and other mmWave base stations to be greater than the battery power consumed by current mmWave base stations; Constraint C4 indicates that P _j (t) and ε _j′j (t) are non-negative number.

For each time slot t, it is necessary to observe the current queue state and take corresponding strategies. According to Lyapunov optimization theory, implementing virtual queue constraints is equivalent to minimizing the drift penalty, and using virtual queue constraints to minimize the objective function is equivalent to minimizing the method defined as "drift plus penalty";

In the above-mentioned step S4, because the virtual queue needs to be introduced when using the Lyapunov optimization method, in the embodiment of the present invention, a discrete-time system model including K queues is also established, as shown in FIG. 4 for details:

First, a brief introduction to the Lyapunov drift formula:

For each time slot t, the number of backlogged tasks in the queue waiting to be processed is Q _k (t), and each queue will generate a new amount of tasks to be processed a _k (t), and can also process the amount of completed tasks b _k (t) ; so in the t+1 time slot, the dynamic change process of the number of tasks backlogged in the queue waiting to be processed is:

Q _k (t+1)=Q _k (t)-b _k (t)+ _ak (t) (10)

Therefore, the sum of the squares of the number of tasks backlogged in the kth queue in time slot t is

The Lyapunov drift is expressed as

Δ(Q(t))=E{L(Q(t+1))-L(Q(t))|Q(t)} (12)

Lyapunov drift is used to measure the expectation of the change value between time slot t and t+1 in the Lyapunov function, which can reflect the stability of the system. Reducing the drift can effectively reduce the variation of the queue, thereby improving the stability of the system.

Next, the virtual queue set in this embodiment is briefly introduced:

Set two virtual queues F _ij (t) and E _j (t), where F _ij (t) is the virtual queue of the video playback waiting time, corresponding to the video playback waiting time of the video device, and E _j (t) is the battery power of the base station The virtual queue corresponding to the battery power of the base station is as follows:

F _ij (t+1)=F _ij (t)+max{f _ij (t)-α,0} (13)

When the constraint C1 of the formula (9) is satisfied, it means that the rate of the virtual queue F _ij (t) is stable; when the constraint C2 of the formula (9) is satisfied, it means that the rate of the virtual queue E _j (t) is stable;

Finally, combine the Lyapunov drift formula and the virtual queue:

The connection vector Z(t) of the virtual queue is defined as:

Z(t)=[F _ij (t), E _j (t)] (15)

Combining (15) with (12) gives:

According to (15) and (16) we get:

In equation (17), Δ(t) is an abbreviation for Δ(Q(t)); c ₁ and c ₂ are constants that satisfy the following constraints for all time slots t:

为某个时隙下从其它毫米波基站转移到当前毫米波基站的电池电量的最大值；

为某个时隙下从当前毫米波基站转移到其它毫米波基站的最大值；e_max为毫米波基站获取可再生能源的最大值；P_max为发射功率的最大值；In formula (19),

is the maximum battery power transferred from other millimeter-wave base stations to the current millimeter-wave base station in a certain time slot;

is the maximum value transferred from the current millimeter-wave base station to other millimeter-wave base stations in a certain time slot; e _max is the maximum value of renewable energy obtained by the millimeter-wave base station; P _max is the maximum value of transmit power;

According to Equation (15), Equation (17) can be converted into

In formula (20), V is the penalty weight, which represents the importance of the objective function to the virtual queue constraint; V≥0;

The corresponding bounds in Eq. (17) are relaxed, and the multi-slot optimization problem Eq. (9) is transformed into a sub-optimization problem under a single slot:

Before optimizing the design of formula (21), it is necessary to prove that the optimization function in formula (21) is a convex function. The specific proof method is:

Judging the constraints of formula (21), rewrite it as:

The left side of the inequality of equation (22) is a convex function, and the optimization function in equation (21) is a convex function, it can be proved that the sub-optimization problem under each time slot is a convex optimization problem; therefore, the tools of convex optimization can be used to solve the equation ( 21) Solve, obtain the corresponding (P _j (t), ε _jj' (t)), and realize the shortest corresponding waiting time.

In the embodiment of the present invention, the waiting time minimization optimization algorithm can refer to Table 1:

Table 1 Waiting Time Minimization Optimization Algorithm

The effect of the present invention will be further described below in conjunction with the simulation results.

1. Simulation experimental conditions:

In order to effectively evaluate the latency optimization method based on renewable energy UND video stream proposed by the present invention, the method is compared with the greedy algorithm without energy cooperation between millimeter wave base stations, and the set simulation parameters refer to Table 2:

Table 2 Simulation parameters

能量获取的过程可建模为概率密度函数为

的平稳随机过程。Since there is no energy cooperation between the millimeter-wave base stations when the greedy algorithm is used for the control experiment, that is,

The process of energy acquisition can be modeled as a probability density function as

a stationary random process.

2. Analysis of simulation results:

Referring to Fig. 5, for the same V, the average energy queue lengths of the millimeter-wave base stations are respectively smaller than when the greedy algorithm is used. Specifically, when n=0.5, the average energy queue length of the millimeter-wave base station is smaller than the length when n=0.9. This suggests that energy cooperation can alleviate the need for battery capacity. Because without energy cooperation, each millimeter-wave base station must store more energy to deal with energy-deficient time slots. Conversely, energy cooperation allows base stations to borrow energy from other base stations to ensure normal data transmission. When the energy arrival rate between the millimeter-wave base stations is low, the energy transmission loss between the millimeter-wave base stations is large, which reduces the average battery power of the millimeter-wave base stations.

Referring to Figures 6 and 7, it is assumed that the amount of renewable energy collected by BS ₁ is 0, that is, e _j (t)=0, so as to simulate harsh environmental conditions. In this case, the small graph in Figure 5 shows that when the greedy algorithm is used, the average waiting time of the video equipment corresponding to BS ₁ is 1 (s/slot); this is because the millimeter-wave base station cannot obtain energy, so the data cannot be transfer to the video device, causing the video device to not work properly due to lack of video data. At this time, because the energy cooperation between the millimeter-wave base stations is adopted between the other two groups, the average waiting time of BS ₁ is obviously less affected and can continue to work, thereby ensuring the video viewing experience of the corresponding users;

For different parameters V, the method proposed in the present invention can ensure that when the millimeter-wave base station cannot normally obtain renewable energy, the corresponding video equipment of the millimeter-wave base station can also work normally; at the same time, the video corresponding to other millimeter-wave base stations can be ignored. The effect of the average wait time of the device. This further proves that the performance of the method proposed in the present invention is better than the greedy algorithm. For most values of V, when n=0.9, the average waiting time of BS ₁ and other millimeter-wave base stations corresponding to video equipment is less than the average waiting time when n=0.5; when n=0.5 or n=0.9, when V When the value of is about 200, the average waiting time of the video equipment corresponding to BS ₁ and other millimeter-wave base stations shows a minimum value. Therefore, the average latency of video equipment can be further reduced by choosing the values of n and V reasonably.

Referring to Figures 8 and 9, it is also assumed that the amount of renewable energy collected by BS ₁ is 0, and BS ₂ and BS ₃ can collect renewable energy normally. It can be seen from FIG. 7 that when the algorithm proposed by the present invention is adopted, the average waiting time of the video equipment corresponding to each millimeter-wave base station gradually decreases and tends to be stable. This shows that when the millimeter-wave base station cannot obtain renewable energy normally, the video equipment corresponding to each base station can be used normally. From Figure 8, when the greedy algorithm is adopted, the average waiting time of the video equipment corresponding to BS ₁ is always 1 (s/slot), while the average waiting time of the video equipment corresponding to BS ₂ and BS ₃ fluctuates within the constraint range . This shows that when the greedy algorithm is used, the video equipment corresponding to the base station that cannot normally obtain renewable energy cannot be used normally.

The embodiments of the present invention provide a method for optimizing the waiting time of video streams based on renewable energy sources, and introduce the Lyapunov optimization algorithm to minimize the average waiting time by decomposing the original multi-slot optimization problem into multiple single-slot sub-problems, and the millimeter wave base station The mutual exchange of energy can be realized through the smart grid, so that the renewable energy obtained by each millimeter-wave base station can be effectively used, which can effectively use the energy to maintain the network stability while reducing the average waiting time of user video equipment. User viewing experience. And the simulation results show that, compared with the greedy algorithm, the method proposed in the present invention can keep the waiting time and the queue length of the energy queue shorter, and at the same time minimize the average waiting time of the user video equipment. More importantly, in the embodiment of the present invention, when the millimeter-wave base station cannot collect renewable energy for a period of time, the video equipment corresponding to each millimeter-wave base station can still work normally, which further proves that the present invention proposes The algorithm can ensure long-term stable operation of video equipment in the network. The simulation results also show that the average waiting time of video equipment can be further reduced by choosing the values of n and V reasonably.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention. Thus, provided that these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (6)

1. Based on renewable energy UND video stream waiting time optimization method, it is characterized in that, comprising: S1. Obtain the downlink data rate at which the video device is connected to the corresponding millimeter-wave base station at time slot t, and the amount of data in the playback buffer of the video device at the beginning of time slot t; S2, according to the downlink data rate and the data amount, obtain the video playback waiting time experienced by the video device in the time slot t due to insufficient data amount; S3. Calculate the battery power change data of the corresponding millimeter-wave base station; S4. Determine the total waiting time of all video devices within the coverage area of the corresponding millimeter-wave base station in time slot t according to the video playback waiting time of the video device; the battery power change data is used as a constraint condition for the total waiting time ; S5, using the Lyapunov optimization method to optimize the total waiting time to obtain a minimum value; The S3 specifically includes: S31. Assume that in time slot t, the energy obtained by the millimeter-wave base station from the renewable energy is e _j (t); the renewable energy obtained during the day has a maximum value e _max ; then

S32. Limit the battery power transferred between the millimeter-wave base stations in the t time slot:

In formula (5), P _j (t) is the transmit power; E _j (t) is the battery power at the current millimeter-wave base station; ε _jj′ (t) is the battery transferred from the current millimeter-wave base station to other millimeter-wave base stations power; ε _j′j (t) is the battery power transferred from other millimeter-wave base stations to the current millimeter-wave base station; n is the energy transmission efficiency, the n∈[0,1]; n×ε _j′j (t) is the total battery power received by the current millimeter-wave base station from other millimeter-wave base stations; Also need to meet the conditions: P _j (t)≥0,ε _j′j (t)≥0,j′≠j (6) S33. Calculate the battery power change data of the corresponding millimeter-wave base station:

In formula (7), t+1 represents the next time in the t slot. 2. The method for optimizing the waiting time of a video stream based on renewable energy UND as claimed in claim 1, wherein the S1 specifically comprises: S11. Suppose there are M millimeter-wave base stations in the network, denoted as BS _j ,j∈1,2,...,M; and each millimeter-wave base station has K _j video devices served by BS _j for playing video , where the i-th video device corresponding to BS _j is denoted as UE _ij ; S12, all the frequency spectrums of BS _j are normalized to 1, and the frequency spectrum occupied by each video equipment and BS _j transmission corresponds to (N _j ) ^-1 ; the construction time slot t is connected to the downlink of the video equipment of BS _j The data rate formula is:

In formula (1), P _j is the output power of the millimeter-wave base station; σ ² is the power level of thermal noise between two millimeter-wave base stations; L _ij (d _ij ) is the video equipment and its corresponding millimeter-wave The distance between base stations is the path loss of d _ij ; G _b and _Gu are the antenna gains of the millimeter-wave base station and the video equipment, respectively; S13: Acquire the amount of data in the playback buffer of the video device at the beginning of the time slot t. 3. The method for optimizing the waiting time of a video stream based on renewable energy UND as claimed in claim 2, wherein the S2 specifically comprises: When the data in the playback buffer of the video device is exhausted, the video device has an experienced video playback waiting time; the video playback waiting time is expressed as:

(2) in the formula, T ₀ is the length of a time slot; B _ij (t) is the amount of data in the playback buffer of the video device when time slot t begins; r _ij (t) is the amount of data in the video The data consumption rate in the device playback buffer; B _ij (t)+R _ij (t) represents the total amount of data that can be used in time slot t. 4. The method for optimizing the waiting time of a video stream based on renewable energy UND as claimed in claim 1, wherein the S4 specifically comprises: S41, calculate the total waiting time of all video devices within the coverage of the corresponding millimeter-wave base station according to formula (2):

S42, describe the multi-slot optimization problem through mathematical expressions, and extract the optimization constraints:

In formula (9), E[ ] is the average of the total waiting time of the video equipment; α is the set parameter α∈[0,1]; Constraint C1 limits the video playback waiting time; Constraint C2 ensures that the battery only needs a limited battery capacity; Constraint C3 limits the battery power obtained from renewable energy and other mmWave base stations to be greater than the battery power consumed by current mmWave base stations; Constraint C4 represents P _j (t) and ε _{j 'j} (t) is a non-negative number. 5. The method for optimizing the waiting time of a video stream based on renewable energy UND as claimed in claim 4, wherein the S5 specifically comprises: S51. Set a virtual queue: Set two virtual queues F _ij (t) and E _j (t), where F _ij (t) is the virtual queue of the video playback waiting time, corresponding to the video playback waiting time of the video device, and E _j (t) is the battery power of the base station The virtual queue corresponding to the battery power of the base station, there are: F _ij (t+1)=F _ij (t)+max{f _ij (t)-α,0} (13)

When the constraint C1 of the formula (9) is satisfied, it means that the rate of the virtual queue F _ij (t) is stable; when the constraint C2 of the formula (9) is satisfied, it means that the rate of the virtual queue E _j (t) is stable; S52. Convert the multi-slot optimization problem into a sub-optimization problem under a single time slot: The connection vector Z(t) of the virtual queue is defined as: Z(t)=[F _ij (t), E _j (t)] (15) Combining (15) with Lyapunov, we get:

According to (15) and (16) we get:

In equation (17), Δ(t) is an abbreviation for Δ(Q(t)); c ₁ and c ₂ are constants that satisfy the following constraints for all time slots t:

In formula (19),

is the maximum battery power transferred from other millimeter-wave base stations to the current millimeter-wave base station in a certain time slot;

is the maximum value transferred from the current millimeter-wave base station to other millimeter-wave base stations in a certain time slot; e _max is the maximum value of renewable energy obtained by the millimeter-wave base station; P _max is the maximum value of transmit power; According to Equation (15), Equation (17) can be converted into

In formula (20), V is the penalty weight, and the V≥0; The corresponding bounds in Eq. (17) are relaxed, and the multi-slot optimization problem Eq. (9) is transformed into a sub-optimization problem under a single slot:

P _j (t)≥0,ε _j′j (t)≥0,j′≠j (21) S54 , performing an optimal design on formula (21). 6. The method for optimizing the waiting time of a video stream based on renewable energy UND as claimed in claim 5, wherein the S54 specifically comprises: S541, judge the constraint condition of formula (21), and rewrite it as:

S542, the left side of the inequality of equation (22) is a convex function, and combined with the optimization function in equation (21) is a convex function, it is clear that the sub-optimization problem under each time slot is a convex optimization problem; S543 , using a convex optimization tool to solve the equation (21), to obtain the corresponding (P _j (t), ε _jj ′(t)), so as to realize the shortest corresponding waiting time.

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