A method, device and medium for maximizing the utility of IRS-assisted aerial VR video service

Abstract

The present invention discloses a method for maximizing the utility of an IRS-assisted aerial VR video service. The method comprises step 1: deploying an IRS-assisted aerial VR streaming media transmission communication system, wherein the transmission communication system comprises an unmanned aerial vehicle (UAV), an IRS and K users, wherein the UAV performs a task within a time range T according to a preset flight trajectory to provide VR video services for the K users on the ground; step 2: in time slot n, obtaining channel coefficients from the UAV to the IRS, from the IRS to the user k, and from the UAV to the user k; step 3: while ensuring fair treatment of users, jointly optimizing the IRS phase shift {Θ k [n]}, time allocation {x k [n]} and bit rate selection {v k [n]}, so as to maximize the total service utility.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method for maximizing the utility of an IRS-assisted aerial VR video service. Background Art

[0002] In recent years, with the widespread application of 5G networks and the advancement of Internet of Things (IoT) technology, the telecommunication network industry has developed rapidly, which has had a profound impact on health, education, smart cities, agriculture and other fields. On this basis, 6G networks are expected to further promote the development of IoT applications that require remote immersive services or are based on virtual reality (VR). However, the popularity of VR and the growing demand for VR streaming have brought new challenges to current wireless communication systems. A major challenge facing VR streaming is that its demand for high-resolution 360° video is much higher than that of traditional streaming applications. For example, the bit rate of 4K 360° video can reach up to 78 megabits per second. In addition, if the delay between the head-mounted display (HMD) and the corresponding 360° video clip exceeds 20 milliseconds, it will seriously affect the user experience, such as dizziness. To meet these challenges, it is crucial to ensure that the 360° video is transmitted within a short transmission window.

[0003] Rapid progress in the fields of aerospace, energy, and artificial intelligence (AI) has driven the development of unmanned aerial vehicles (UAVs), which have become an ideal choice for performing various tasks due to their high maneuverability and flexibility. For example, UAVs can serve as aerial base stations or relays to provide adaptive video streaming services to ground users. In addition, drones equipped with advanced cameras can also serve as new aerial users that can be seamlessly integrated into existing networks to provide a variety of aerial streaming services including real-time video surveillance, target tracking, and aerial photography.

[0004] Although UAVs bring many advantages to VR streaming systems, there are still some challenges in their application. Especially in complex urban environments, the line-of-sight (LoS) channel between UAVs and ground users is easily obstructed, resulting in a significant degradation in communication performance and a poor user experience. Therefore, how to provide users with smooth video transmission while ensuring the quality of service (QoS) has become a key challenge. To address this challenge, the Intelligent Reflecting Surfaces (IRS) technology is introduced to effectively improve the quality of the air-to-ground (A2G) channel by optimizing the signal propagation path. Specifically, by deploying the IRS in a high-visibility position, a new reconstructed UAV-IRS-user channel is established. Then the intelligent controller independently adjusts the phase and amplitude of each reflective unit according to specific needs, dynamically reshapes the signal propagation environment, and thus improves the performance of air-to-ground (A2G) communication.

[0005] However, for the optimization problem of IRS-assisted aerial VR video services, the optimization objectives of many existing techniques only ensure the overall service utility, which may lead to unfair treatment of some users, resulting in a poor service experience. Summary of the invention

[0006] In view of the problems of the background technology, the present invention provides a method for maximizing the utility of an IRS-assisted aerial VR video service, the method comprising the following steps:

[0007] Step 1: Deploy an IRS-assisted aerial VR streaming media transmission communication system. The transmission communication system includes a UAV, an IRS, and K users. The UAV performs tasks within a time range T according to a preset flight trajectory to provide VR video services to K users on the ground. The transmission communication system uses a time division multiple access protocol to divide the time range T into N time slots, and each time slot is divided into K sub-time slots to ensure that each user can receive services in different time periods, thereby avoiding mutual interference between users. Each VR streaming video frame is divided into I max video blocks, for any user k, at the start time of time slot n, an uplink request is initiated to the UAV, and the index of the video block that user k wants to serve is specified. The requested index set is expressed as in, Representing a collection The cardinality, symbol The formula is the definition formula; Indices representing all video chunks for each streaming video frame;

[0008] Step 2: In time slot n, obtain the channel coefficients from UAV to IRS, IRS to user k, and UAV to user k, which are expressed as and h u,k [n], where M represents the number of reflective elements in the IRS, represents the set of complex numbers, n∈{1,...,N}, k∈{1,...,K};

[0009] Step 3: Jointly optimize the IRS phase shift {Θ k [n]}, time allocation {x k [n]} and bit rate selection In order to maximize the total service utility, represents the phase shift matrix of IRS for user k in time slot n, where represents the phase shift of the mth reflector element in the IRS, m∈{1,...,M}; x k [n]∈[0,1], represents the proportion of service time allocated to user k in time slot n; vector represents the bitrate choice for all video chunks requested by user k in time slot n, represents the data bit rate requested by user k for video block i in time slot n.

[0010] Furthermore, the problem of maximizing the total service utility in step 3 is denoted as problem P1 and is expressed as follows:

[0011]

[0012] J F ≥η 0 ,

[0013] in,

[0014] Indicates the minimum bit rate of the video block; Indicates the maximum bit rate of the video block; Represents a collection of users; represents a set of time slots;

[0015] represents the total number of bits obtained by user k in time slot n, W represents the system bandwidth, P represents the transmission power of UAV, σ 2 represents the noise power, δ represents the length of each time slot, and h k [n] = h u,k [n]+h r,k [n]Θ k[n]g[n], represents the channel coefficient between UAV and user k in time slot n;

[0016] represents the total number of bits required by user k in time slot n; T v Indicates the duration of a video block;

[0017] Used to characterize the fairness of the average service utility among users; η 0 represents the fairness threshold;

[0018] represents the average service utility of user k during the service period;

[0019] represents the utility of the aerial VR streaming service obtained by user k in time slot n, κ intra is the quality switching loss within the video frame The scaling factor of intra >0;

[0020] represents the intra-frame quality switching loss of user k in time slot n.

[0021] Furthermore, the method further comprises: splitting the problem P1 into two sub-problems, namely, the phase shift optimization problem of IRS and the optimization problem of bit rate selection and time allocation.

[0022] Furthermore, the method includes: converting the phase shift optimization problem of the IRS into a problem P2, and the problem P2 is expressed by a formula:

[0023]

[0024] Find the optimal phase shift of the reflective element m of the IRS in time slot n for any user k Then we get the IRS phase shift {Θ k [n]}, where the optimal phase shift The superscript H indicates the conjugate transpose.

[0025] Furthermore, the method also includes optimizing bit rate selection and time allocation using SCA technology, including:

[0026] S1: The optimization problem of bit rate selection and time allocation is transformed into problem P3, which is expressed as:

[0027]

[0028] J F ≥η0 ;

[0029] S2: Introducing slack variables {ξ k,i [n]}, transforming problem P3 into problem P4. Problem P4 is expressed as:

[0030]

[0031]

[0032] in,

[0033] S3: Problem P4 is transformed into problem P5, and problem P5 is solved by an iterative optimization method, thereby obtaining an iterative optimal solution for bit rate selection and time allocation, wherein problem P5 is expressed by the formula:

[0034]

[0035] in, represents the v of the rth iteration k,i [n],

[0036]

[0037] The present invention also provides a processor, which implements the steps of the above method when executing a program.

[0038] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0039] The present invention also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0040] The beneficial effects of the above technical solution are:

[0041] The present invention proposes an IRS-assisted aerial VR streaming framework, which aims to maximize the overall service utility through the joint optimization of time allocation, bitrate selection and IRS phase shift, while ensuring fairness among users; since the present invention characterizes the problem as a non-convex optimization problem through a mathematical model, it is difficult to solve it directly. In order to effectively solve the above challenges, the present invention splits the original problem into two sub-problems and handles them separately. Specifically, the closed-form solution of the optimal phase shift is first obtained by mathematical derivation, and then the successive convex approximation (SCA) technology is applied to optimize the bitrate selection and time allocation. Based on these methods, the present invention further proposes an effective iterative optimization algorithm to obtain a near-optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings of the present invention are as follows:

[0043] Figure 1 is a scene diagram of the system of the present invention;

[0044] Figure 2 It is the simulation result of the relationship between the achievable rate or time allocation ratio and the flight time;

[0045] Figure 3 It is the simulation result of the relationship between the total service utility and the fairness threshold or the number of IRS elements. DETAILED DESCRIPTION

[0046] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0047] The present invention discloses a method for maximizing the utility of an IRS-assisted aerial VR video service, the method comprising the following steps:

[0048] Step 1: Deploy an IRS-assisted aerial VR streaming media transmission communication system, which includes an unmanned aerial vehicle (UAV), an IRS, and K users. The UAV performs tasks within a time range of T according to a preset flight trajectory to provide VR video services for K users on the ground. The transmission communication system adopts a time division multiple access protocol to divide the time range T into N time slots, and each time slot is divided into K sub-time slots to ensure that each user can receive services in different time periods, thereby avoiding mutual interference between users.

[0049] Specifically, in this system, a UAV equipped with a panoramic camera performs a cruise mission and captures 360° videos. The captured videos are processed through advanced encoding technology to generate multi-bitrate data streams, which are transmitted to multiple users on the ground. These users use a collection of Represents. Each user's HMD and UAV are equipped with a single antenna. In order to improve the obstruction of the LOS channel between the UAV and the ground users caused by dense obstacles such as trees and buildings, the IRS is deployed on the facade of a building. Such a deployment helps to establish a LOS channel between them, thereby assisting the UAV to better serve the ground users. Assume that the UAV performs a cruising mission on a fixed trajectory with a time range of T, and the trajectory can be determined in advance according to the requirements of the mission. For ease of analysis, the present invention divides the time T into N time slots, where the length δ of each time slot can be set small enough so that the position of the UAV in each time slot can be considered to remain unchanged. Each VR streaming video frame is divided into I max video blocks, for any user k, at the start time of time slot n, an uplink request is initiated to the UAV, and the index of the video block that user k wants to serve is specified. The requested index set is expressed as in, Representing a collection The cardinality, symbol The formula is the definition formula; Indices representing all video chunks for each streaming video frame;

[0050] Step 2: In time slot n, obtain the channel coefficients from UAV to IRS, IRS to user k, and UAV to user k, which are expressed as and h u,k [n], where M represents the number of reflective elements in the IRS, represents the set of complex numbers, n∈{1,...,N}, k∈{1,...,K};

[0051] Step 3: Jointly optimize the IRS phase shift {Θ k [n]}, time allocation {x k [n]} and bit rate selection {v k [n]}, so as to maximize the total service utility, where represents the phase shift matrix of IRS for user k in time slot n, where represents the phase shift of the mth reflector element in the IRS, m∈{1,...,M}; x k [n]∈[0,1], represents the proportion of service time allocated to user k in time slot n; vector v k[n]=[v k,i [n], represents the bitrate choice of all video chunks requested by user k in time slot n, v k,i [n] represents the data bit rate requested by user k for video chunk i in time slot n.

[0052] The method of the present invention is described in detail below.

[0053] Without loss of generality, the present invention adopts a three-dimensional Cartesian coordinate system to perform spatial position analysis, wherein the coordinates of user k and IRS are respectively expressed as and The coordinates of the UAV in time slot n can be expressed as q[n] = [x[n], y[n], z[n]] T , where n∈{1,...,N}. IRS is equipped with a A uniform planer array (UPA) is composed of reflective elements, where N z and M z Respectively represent the number of elements along the x-axis and the z-axis. represents the phase shift matrix of IRS for user k in time slot n, where represents the phase shift of the mth reflective element, where m∈{1,...,M}. In order to further utilize the potential of IRS beamforming to assist video transmission, the present invention further divides each time slot into K sub-time slots, each sub-time slot corresponding to a specific beamforming mode of a specific user.

[0054] In the present invention, the UAV is fully aware of the corresponding channel state information (CSI). It is worth noting that the direct communication channel between the UAV and the ground user is often hindered by obstacles such as buildings and trees in complex environments. Therefore, the communication channel from the UAV to user k can be modeled using the Rayleigh fading model as follows:

[0055]

[0056] Among them, α 1 represents the path loss exponent of the UAV-to-user channel, d u,k [n]=||q[n]-q k || represents the distance between the UAV and the user at time slot n, ρ represents the channel gain at the reference distance of 1 meter, Following an independent and identically distributed cyclically symmetric complex Gaussian (CSCG) distribution, its mean is 0 and its variance is 1, which can be expressed as The beam vector function of a uniform planar array (UPA) with X×Y elements can be expressed as:

[0057]

[0058] Where d is the element spacing, β is the elevation angle, φ is the horizontal azimuth angle, and λ is the carrier wavelength. Denotes the Kronecker product. Considering that both the UAV and the IRS are at higher positions, it is assumed that there is a pure LoS channel between the UAV and the IRS. Therefore, the channel between the UAV and the IRS can be modeled as:

[0059]

[0060] Among them, α 2 represents the path loss index of the channel, d u,r [n]=||q[n]-q r || represents the distance between UAV and IRS, d r represents the spacing between IRS elements, β u,r [n] and φ u,r [n] represents the elevation angle and azimuth angle from UAV to IRS at time slot n. Specifically,

[0061] Compared with the UAV-IRS channel, the channel from IRS to ground users is more complex, including two components, LoS and NLoS. The present invention uses the Rician fading model to model this channel, namely:

[0062]

[0063] Among them, α 3 represents the path loss index of the channel, d r,k [n]=||q r -q k || represents the distance from IRS to ground user k, and G is the Rician factor. Specifically, where β r,k and φ r,k denote the elevation angle and horizontal azimuth angle from IRS to user k, respectively, On the other hand, the present invention converts the NLoS component It is modeled as a cyclically symmetric complex Gaussian distribution with mean 0 and variance 1, expressed as

[0064] In order to effectively solve the interference problem between ground users, the present invention adopts the time-division multiple access (TDMA) protocol. Each time slot is divided into K sub-time slots to ensure that each user can receive services in different time periods, thereby avoiding mutual interference between users. Specifically, define x k [n]∈[0,1] represents the proportion of service time allocated to user k in time slot n, where The final channel coefficient between UAV and user k in time slot n is expressed as h k [n] = h u,k [n]+h r,k [n]Θ k [n]g[n]. Therefore, the total number of bits obtained by user k in time slot n can be expressed as:

[0065]

[0066] Where W is the system bandwidth, P is the transmission power of the UAV, and σ 2 represents the noise power, and δ represents the length of each time slot.

[0067] Considering the user's demand for VR video quality and the limitation of UAV onboard resources, the present invention constructs a video transmission optimization framework. First, each 360° VR video frame is divided into I max video blocks, according to N x Row and N y The columns are arranged and can be expressed as Will is defined as the index of all video blocks in the frame. For user k, at the beginning of time slot n, an uplink request is sent to the UAV, specifying the index of the video block it wishes to serve. These video block indices are determined by the visual range of user k, and the requested index set can be expressed as in Representing a collection The cardinality of .

[0068] In order to balance the user's demand for video quality with the limited UAV onboard resources, it is assumed that the video block bit rate requested by the user can only be within a specific range. arrive Specifically, the present invention uses v k,i [n] represents the data bit rate requested by user k for video block i in time slot n, then the following constraints can be obtained:

[0069]

[0070] When the UAV receives the user's request, it needs to make a decision on the bit rate of each video block. Use vector v k [n]=[v k,i [n], represents the bit rate selection of all video blocks requested by user k in time slot n. It is worth noting that the user experience quality will be affected by the bit rate switching of different video blocks in the same video frame. If the bit rate switching between different video blocks occurs in a specific time slot, it may cause a decrease in the user experience quality, which is usually manifested as a loss of intra-frame quality. The present invention defines the intra-frame quality switching loss of user k in time slot n as Specifically expressed as:

[0071]

[0072] Therefore, the utility of the aerial VR streaming service obtained by user k in time slot n is expressed as:

[0073]

[0074] in, κ intra is the intra-frame quality switching loss The scaling factor of intra > 0. In fact, it is always guaranteed that is non-negative, because if If is negative, it can be made non-negative by adjusting the scaling factor to 0. Without loss of generality, the rest of the present invention will be omitted for simplicity of description. + .

[0075] The total number of bits required by user k in time slot n is given by Given, where T v represents the duration of a 360° video block. To ensure that user k can receive all data requests in each time slot, user k obtains a total number of bits J in time slot n. k [n]The following QoS constraints must be met:

[0076]

[0077] The present invention aims to solve the problem of maximizing the service utility in an IRS-assisted aerial VR streaming system. However, if we only focus on maximizing the total service utility, some users will be treated unfairly, resulting in a poor user experience. To alleviate this problem, the present invention introduces the Jain fairness index as an indicator to measure the fairness of the service. Assume represents the average service utility of user k during the service period, then the fairness of the average service utility among users is described by the Jain fairness equation:

[0078]

[0079] Among them, J F ∈[1 / K,1], the higher the value, the more similar the service utility between users is, and the higher the fairness of the service utility between users is. In order to ensure that the service utility between users is always maintained at a high level, the present invention introduces a constraint condition, that is, the Jain fairness index is always greater than a threshold η 0 Therefore, the goal of the present invention is to jointly optimize the IRS phase shift {Θ k [n]}, time allocation {x k [n]} and bit rate selection {v k [n]} to maximize the total service utility. This optimization problem can be expressed as problem P1, namely:

[0080]

[0081] J F ≥η 0 .(11e)

[0082] Among them, constraint (11a) restricts the bit rate of the video block to a specific range; constraint (11b) sets specific restrictions on time allocation; constraint (11c) gives the QoS requirements for video transmission; constraint (11d) limits the range of IRS phase shift; constraint (11e) is committed to ensuring fairness of service among users. This problem is a non-convex optimization problem with complex fractional constraints and is difficult to solve directly.

[0083] Therefore, for question P1,

[0084] First, it is split into two sub-problems, namely, the phase shift optimization problem of IRS and the optimization problem of bit rate selection and time allocation. For these two sub-problems, corresponding solutions are then proposed respectively. One is to optimize the IRS phase shift using a closed-form solution-based method, and the other is to optimize the bit rate selection and time allocation using SCA technology. Then, the present invention proposes an efficient iterative optimization algorithm, which aims to gradually obtain the suboptimal solution of problem P1 by optimizing the sub-problems.

[0085] First, for the phase shift optimization problem of IRS, that is, considering the choice of {v k [n]} and time allocation {x k [n]}, the optimized phase shift {Θ k [n]}.

[0086] According to constraint (11c), the phase shift of IRS only affects the total number of bits obtained by user k in time slot n, i.e., J k [n]. Therefore, the phase shift optimization problem for any user k in time slot n can be transformed into problem P2, which is expressed as:

[0087]

[0088] According to formula (5), when the bit rate is selected {v k [n]} and time allocation {x k [n]} are given, except |h k |n|| 2 All variables except are constants. In addition, it can be verified that J k [n] is about |h k |n|| 2 Therefore, the objective function of problem (P2) can be equivalent to maximizing |h k |n|| 2 , which is equivalent to maximizing Although this is still a non-convex optimization problem, its specific structure enables a closed-form solution to be obtained through equivalent transformations. Specifically, the following equation can be established:

[0089]

[0090] in, Therefore, the right side of formula (13) can be expressed as:

[0091]

[0092] This equation holds at (a) if and only if That is, the angle of each item is the same. Without loss of generality, assume that θ k,* = 0. Therefore, for any user k, the optimal phase shift of the reflective element m of the IRS in time slot n can be expressed as:

[0093]

[0094] Wherein, the superscript H represents the conjugate transpose, and arg() represents the argument (phase angle) of the complex number.

[0095] because Therefore, according to Then we can get Θ k [n], for any k and n, we can get the optimal solution of the IRS phase shift {Θ k [n]}.

[0096] After the optimal phase shift is obtained, the optimization of bit rate selection and time allocation may include the following steps:

[0097] S1: Set the bit rate selection {v k |n|} and the time distribution {x k [n]} The optimization sub-problem is expressed as problem P3, namely:

[0098]

[0099] st(11a),(11b),(11c),(11e).

[0100] It can be verified that the intra-frame quality switching loss is the bit rate selection vector v k [n] is a convex function, and the service utility is a concave function. However, due to the non-convex constraint (11e), problem (P3) is still a non-convex optimization problem that is difficult to solve directly. In order to meet this challenge, the present invention transforms constraint (11e) into the following form:

[0101]

[0102] S2: Introducing slack variables {ξ k,i [n]}, transforming problem (P3) into problem (P4), namely:

[0103]

[0104] Among them, the slack variable {ξ k,i [n]}Satisfies:

[0105]

[0106] in,

[0107] It is easy to verify that in the optimal solution of problem (P4), the equations in constraint (17) are all satisfied. k,i [n] can be increased without reducing the objective value of problem (P4) while ensuring that all other constraints are satisfied. Therefore, problem (P4) is equivalent to problem (P3).

[0108] S3: transforming problem (P4) into problem (P5), and solving problem (P5) by an iterative optimization method, thereby obtaining an iterative optimal solution for bit rate selection and time allocation.

[0109] Specifically, in order to handle constraint (17), the present invention treats the convex term in the constraint in the rth iteration as Applying the first-order Taylor approximation, we can obtain:

[0110]

[0111] in, at this time, is a linear function. On the other hand, the constraint (18) about is convex, and its lower bound can be obtained by first-order Taylor approximation, namely:

[0112]

[0113] Among them, Γ lb is a vector v k [n] is a concave function. Based on the above discussion, problem (P4) can be approximately transformed into problem (P5), namely:

[0114]

[0115]

[0116] At this point, problem (P5) is a standard convex optimization problem and can be solved efficiently using convex optimization tools such as CVX.

[0117] The detailed process of solving problem (P1) is given in Algorithm 1. It is as follows:

[0118]

[0119]

[0120] It can be found that after step 4 of Algorithm 1, constraint (11c) is appropriately relaxed to maximize the total number of bits obtained by user k in each time slot n, thus giving the variable v in problem (P4) k (n) provides a larger optimization space, so that the target value can be further improved. The computational complexity of the algorithm depends on the number of design variables and can be expressed as Where I represents the number of iterations required to reach convergence.

[0121] A large number of simulation experiments are carried out to verify the effectiveness of the proposed scheme.

[0122] Given an aerial VR video transmission scenario, such as Figure 1As shown in the figure, the UAV performs the cruise mission according to the predetermined flight trajectory and provides VR experience for K = 5 users on the ground. The ground users are randomly distributed in a square with a side length of 600 meters, and an IRS is deployed on a 25-meter-high building. Similarly, the relevant channel parameters are set as follows: α 1 =3.8, α 2 =2.2, α 3 =2.8, ρ=-30dB, P=1W, f c =2.4GHz,λ=c / f c , where c represents the speed of light, d = λ / 2, W = 1000MHz, σ 2 =-120dBm. Algorithm-related parameters are set to ∈=10 -3 Unless otherwise specified, other relevant parameters are set to N x =6, N y =8, T = 30s, δ = 1s, k intra =10.

[0123] Figure 2 (a) shows that when M x =10, M y =7, and η 0 = 0.6, the achievable rates of different users after optimizing the phase shift of IRS. Figure 2 It can be observed in (a) that as the UAV moves along the flight trajectory, the user's achievable rate increases first and then decreases. This is due to the change in the distance between the UAV, the user, and the IRS throughout the flight. Specifically, in the first half of the UAV flight, the distance between the UAV and the user, and between the UAV and the IRS gradually decreases, thereby enhancing the channel quality and thus improving the user's achievable rate. In the second half of the UAV flight, as the distance between the two gradually increases, the channel quality gradually decreases, and the user's achievable rate also decreases. Figure 2 (b) shows the relationship between the time allocation ratio and the flight time during the flight. Figure 2 Compared with (a), it can be seen that there is an inverse relationship between the user's achievable rate and the service time they obtain. Specifically, users with higher achievable rates are allocated less service time, and vice versa. This phenomenon stems from the fairness consideration of system resources, which compensates for the differences in achievable rates among users and also ensures the fairness of service utility among users. In addition, Figure 2(b) The initial fluctuation of the curve reflects the distribution of service time after dynamic adjustment when the achievable rates of users 1, 2, and 5 are almost the same under the constraints of the Jain fairness equation. This adjustment is to ensure that users in the network can obtain a balanced service experience and avoid other users with high achievable rates occupying too much service time, thereby suppressing the service opportunities of low-rate users.

[0124] To better illustrate the effect of the IRS-assisted aerial VR streaming system, the present invention compares the performance of the proposed scheme with the following three benchmark schemes: balanced service time scheme, IRS random reflection scheme, and no IRS scheme.

[0125] Figure 3 (a) shows that when M x =10,M y =4. It can be seen that the solution proposed by the present invention performs best among all the benchmark solutions, and its performance improvement is more significant when the fairness threshold is low. As the fairness threshold increases, the total service utility decreases, which reflects the basic trade-off between fairness and service utility. In other words, the improvement of fairness is, to a certain extent, at the expense of the total service utility. Figure 3 (b) shows that when η 0 =0.6. It can be seen that the solution proposed in the present invention achieves the highest service utility. This is because the use of IRS can obtain a larger passive beamforming gain, thereby enhancing the signal received by the user. As the number of reflective elements increases, the performance improvement effect is more obvious. However, as the number of IRS elements increases to 70, the total service utility almost stops increasing. This is because the fairness threshold η 0 and maximum bit rate selection The total service utility tends to be saturated due to the limitation of IRS. In addition, although the performance of the IRS random reflection scheme is improved compared with the scheme without IRS, it is still lower than the scheme proposed in the present invention, which shows that the potential of IRS reflection optimization is not fully utilized in the IRS random reflection scheme to assist VR video transmission.

[0126] The present invention also provides a processor, which implements the steps of the above-mentioned IRS-assisted aerial VR video service utility maximization method when executing a program.

[0127] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the above-mentioned IRS-assisted aerial VR video service utility maximization method are implemented.

[0128] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned IRS-assisted aerial VR video service utility maximization method.

[0129] In summary, the present invention proposes an IRS-assisted aerial VR streaming optimization method. In order to maximize the total service utility of users by jointly optimizing bitrate selection, time allocation and IRS phase shift while meeting the fairness threshold and QoS requirements. Specifically, the present invention first splits the original non-convex problem into two tractable sub-problems, and then applies closed-form solutions and SCA technology to process them respectively, and proposes an efficient iterative algorithm that can ensure its convergence. The simulation results show the performance improvement of the proposed scheme compared with the baseline scheme, and reveal the trade-off between service utility and fairness.

[0130] Finally, it should be noted that the embodiments described in this specification are merely illustrative of the present invention, and those skilled in the art should understand that various changes may be made in form and detail without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A method for maximizing the utility of IRS-assisted aerial VR video service, characterized in that: include: Step 1: Deploy an IRS-assisted aerial VR streaming media transmission communication system. The transmission communication system includes a UAV, an IRS, and K users. The UAV performs tasks within a time range T according to a preset flight trajectory to provide VR video services to K users on the ground. The transmission communication system uses a time division multiple access protocol to divide the time range T into N time slots, and each time slot is divided into K sub-time slots to ensure that each user can receive services in different time periods, thereby avoiding mutual interference between users. Each VR streaming video frame is divided into I max video blocks, for any user k, at the start time of time slot n, an uplink request is initiated to the UAV, and the index of the video block that user k wants to serve is specified. The requested index set is expressed as in, Representing a collection The cardinality, symbol The formula is the definition formula; Indices representing all video chunks for each streaming video frame; Step 2: In time slot n, obtain the channel coefficients from UAV to IRS, IRS to user k, and UAV to user k, which are expressed as and h u,k [n], where M represents the number of reflective elements in the IRS, represents the set of complex numbers, n∈{1,...,N}, k∈{1,...,K}; Step 3: Jointly optimize the IRS phase shift {Θ k [n]}, time allocation {x k [n]} and bit rate selection {v k [n]}, so as to maximize the total service utility, where represents the phase shift matrix of IRS for user k in time slot n, where represents the phase shift of the mth reflector element in the IRS, m∈{1,...,M}; x k [n]∈[0,1], represents the proportion of service time allocated to user k in time slot n; vector represents the bitrate choice of all video chunks requested by user k in time slot n, v k,i [n] represents the data bit rate requested by user k for video block i in time slot n; The problem of maximizing the total service utility in step 3 is denoted as problem P1 and is expressed as follows: J F ≥η0, Where: v min Indicates the minimum bit rate of the video block; v max Indicates the maximum bit rate of the video block; Represents a collection of users; represents a set of time slots; represents the total number of bits obtained by user k in time slot n, W represents the system bandwidth, P represents the transmission power of UAV, σ 2 represents the noise power, δ represents the length of each time slot, and h k [n] = h u,k [n]+h r,k [n]Θ k [n]g[n], represents the channel coefficient between UAV and user k in time slot n; represents the total number of bits required by user k in time slot n; T v Indicates the duration of a video block; It is used to characterize the fairness of the average service utility among users; η0 represents the fairness threshold; represents the average service utility of user k during the service period; represents the utility of the aerial VR streaming service obtained by user k in time slot n, κ intra is the quality switching loss within the video frame The scaling factor of intra >0; represents the intra-frame quality switching loss of user k in time slot n.

2. The method for maximizing the utility of an IRS-assisted aerial VR video service according to claim 1, characterized in that: The method further comprises: splitting the problem P1 into two sub-problems, namely, the phase shift optimization problem for IRS and the optimization problem for bit rate selection and time allocation.

3. The method for maximizing the utility of IRS-assisted aerial VR video service according to claim 2, characterized in that: The method includes: converting the phase shift optimization problem of IRS into problem P2, and problem P2 is expressed by a formula: Find the optimal phase shift of the reflective element m of the IRS in time slot n for any user k Then we get the IRS phase shift {Θ k [n]}, where the optimal phase shift The superscript H indicates the conjugate transpose.

4. The method for maximizing the utility of an IRS-assisted aerial VR video service according to claim 2, characterized in that: The method also includes optimizing bit rate selection and time allocation using SCA technology, including: S1: The optimization problem of bit rate selection and time allocation is transformed into problem P3, which is expressed as: J F ≥η0; S2: Introducing slack variables {ξ k,i [n]}, transforming problem P3 into problem P4. Problem P4 is expressed as: in, S3: Problem P4 is transformed into problem P5, and problem P5 is solved by an iterative optimization method, thereby obtaining an iterative optimal solution for bit rate selection and time allocation, wherein problem P5 is expressed by the formula: in, represents the v of the rth iteration k,i [n], 5. A processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 4 are implemented.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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