Intelligent reflecting surfaces (IRSs) mounted on maneuverable aerial platforms to form aerial IRS (AIRS) relays represent a novel paradigm for large-scale downlink transmission in smart cities. However, the challenge of multivariate dynamic coupling hinders most existing studies due to high computational complexity and limited scalability. To address these issues, this paper proposes a soft-attention-actor-critic (SAAC) optimization framework that efficiently decomposes the joint optimization of multi-AIRS deployment, passive beamforming, and active beamforming at the base station into two sequential subproblems. The objective is to maximize average downlink spectral efficiency and service fairness, while minimizing deployment energy consumption. In the first stage, a conservative lower bound of spectral efficiency is formulated to guide multiple AIRSs toward near-optimal deployment positions. In the second stage, refined optimization is performed for both passive and active beamforming matrices. Furthermore, multi-head attention modules are incorporated into the critic and actor networks in each phase, enabling AIRS to adaptively attend to the observations and actions of other agents, and enhancing the ability to handle high-dimensional observation-action spaces. Extensive simulation results validate that the proposed SAAC framework consistently outperforms mainstream deep reinforcement learning baselines across diverse network conditions, highlighting its superior performance and scalability.

Vehicular edge computing (VEC) enhances computational efficiency by strategically offloading tasks from vehicles to edge servers. Integrating semantic communication into vehicular networks introduces further benefits by leveraging semantic information to reduce task transmission delays. However, semantic-aware computation offloading encounters dual challenges: adaptively selecting semantic features to preserve task-critical meaning and dynamically allocating communication and semantic resources under varying network conditions. To cope with these challenges, we propose an importance-based hybrid-action multi-agent proximal policy optimization (I-HAMAPPO) algorithm for the semantic-aware vehicular computation offloading system in this paper. By assessing the importance scores of semantic features, an importance evaluation module (IEM) is designed to selectively transmit task-relevant information. A utility function, integrating task delay, energy consumption, and semantic similarity, is developed to provide a multi-dimensional performance evaluation of the system. Subsequently, the optimization problem is formulated with the objective of maximizing system utility by optimizing communication resources and semantic compression ratios. Considering the presence of mixed decision variables in the formulated problem, we employ the proposed I-HAMAPPO algorithm to optimize the continuous and discrete actions jointly. Based on real-world vehicle trajectories from the highD dataset, extensive experimental results demonstrate the convergence of I-HAMAPPO and its efficacy in maximizing system utility.

Mobile crowdsensing (MCS) enables data collection by leveraging the sensing capabilities of distributed mobile devices (MDs). However, its performance is often constrained by limited coverage and connectivity of ground-based sensors. To overcome these limitations, this paper presents an efficient data collection framework in unmanned aerial vehicle (UAV)-assisted MCS systems. The proposed framework leverages the aerial mobility of UAVs to enhance the spatial coverage of MCS. By optimizing flight trajectories of UAVs, we aim to balance the age of information (AoI) and energy consumption. Specifically, this work considers the mobility of MDs and introduces a communication-enhanced trajectory optimization (CETO) algorithm to improve UAV coordination and adaptability in dynamic environments. Simulation results demonstrate that the proposed algorithm significantly outperforms other mainstream baseline methods employing deep reinforcement learning (DRL).

This paper investigates the joint design of the transmit beamforming matrix and the intelligent reflecting surface (IRS) phase shift matrix in a multi-user, multiple-input singleoutput (MU-MISO) system integrated with an aerial IRS. The proposed approach leverages the high mobility and flexibility of unmanned aerial vehicles (UAVs) to enhance the probability of a line-of-sight (LoS) link, thereby maximizing the sum spectral efficiency of the user equipments. Specifically, to adapt to the time-varying nature of real-world communication environments, we propose a twin delayed deep deterministic policy gradient (TD3) algorithm incorporating self-attention mechanisms for the joint optimization of beamforming and phase shift strategies. Furthermore, the batch normalization technique is employed to improve the algorithm's capability to process extensive state and action spaces, thereby accelerating convergence. Simulation results demonstrate that the proposed algorithm outperforms other mainstream deep reinforcement learning (DRL)-based baseline methods.

The expansion of the Internet of vehicles (IoV) has spurred a significant increase in the demand for vehicular computation tasks, posing challenges for in-vehicle task processing. Multi-access edge computing (MEC), which is intended for low-latency task execution, experiences sub-band competition and workload imbalance due to the uneven distribution of vehicle densities. This paper presents a novel IoV architecture leveraging multi-roadside-unit (RSU) capabilities to facilitate efficient load balancing among RSUs through edge-to-edge collaboration. The optimization problem of computation offloading is formulated by minimizing overall task delay, which is further decoupled into two sub-problems: communication resource allocation and load balancing. We devise a two-stage deep reinforcement learning-based communication resource allocation and load balancing (DRLCL) algorithm to tackle these sub-problems sequentially. Based on real-world vehicle trajectories, experimental evaluations reveal that our proposed algorithm outperforms the baselines in reducing overall delay.

With the rapid evolution of sixth-generation (6G) wireless communication, multi-user multiple-input multiple-output (MIMO) systems face challenges such as insufficient channel estimation accuracy and excessive pilot overhead in complex propagation environments. Reconfigurable intelligent surface (RIS) technology provides a promising solution by dynamically altering wireless channel characteristics. In this paper, we propose a location-embedded graph attention network (LE-GAT), which models user nodes and RIS nodes as distinct types in a graph. An attention mechanism is introduced for adaptive information aggregation among nodes, while location information is embedded to enhance spatial feature representation. The proposed end-to-end learning framework directly predicts the optimal beamforming matrix and RIS reflection vector from received pilot signals, eliminating the need for explicit and computationally expensive channel estimation. Simulation results demonstrate that the proposed method outperforms benchmark algorithms in sum-rate maximization, particularly under pilot-limited scenarios.

Integrated satellite-terrestrial networks (ISTNs) enable global connectivity but face challenges in efficient resource allocation due to increasing service demands. To address Quality of Service (QoS) degradation caused by inefficient resource allocation in ISTN's heterogeneous network, we propose a network slicing (NS) resource allocation algorithm based on deep reinforcement learning (DRL). First, an ISTN system model is constructed using NS, along with an evaluation approach for slicing services. Next, a satisfaction utility function is defined to quantify the QoS of slicing services, and an optimization problem is formulated. Then, based on the Markov decision process (MDP) and dueling double deep Q-learning (D3QN) theory, an NS resource allocation algorithm is designed, comprising both training and execution phases. Simulation results demonstrate that the proposed algorithm outperforms baseline approaches in system satisfaction, bandwidth allocation, and satellite network utilization.

Forest fire poses formidable challenges for real-time victim localization and regional coverage using unmanned aerial vehicles (UAVs). Existing approaches often suffer from fragmented task modeling and limited adaptability under constraints such as energy, communication, and obstacles. To address these issues, we propose a proximal policy optimization (PPO)-based cooperative search framework that formulates static target detection and area coverage as a unified multi-agent markov decision process (MDP). The framework incorporates coverage-driven state modeling, a layered multi-objective reward function, and shared knowledge fusion to enhance inter-agent coordination. Simulation results demonstrate that our method consistently outperforms baseline approaches such as federated multi-agent deep deterministic policy gradient (FMADDPG) and deep network q-mixing (DNQMIX) in cumulative reward, collision avoidance, target capture rate, and area coverage, validating its practicality for real-world forest fire rescue operations.

In recent years, semantic communication has received a lot of attention due to its ability to solve the challenges faced by traditional communication systems. However, little attention has been paid to the fact that during data compression and transmission, the lost data can be reconstructed by neural networks to improve transmission efficiency. In order to solve the impact of the loss of semantic information on the transmission performance in vehicular networks, this paper proposes a goal-oriented communication based on semantic reconstruction (GOCSR). By designing a semantic reconstruction network at the receiver, the lost semantic information is predicted and reconstructed, and then the complete semantic information is used to perform downstream tasks. To evaluate the efficiency of GOCSR, extensive simulation experiments are conducted using the Cityscapes dataset. Simulation results show that GOCSR can achieve higher target execution performance than the existing semantic communication schemes.

In the realm of the Internet of Things (IoT), computation offloading confronts challenges in remote areas due to scarce general-purpose edge/cloud infrastructure and insufficient terrestrial network coverage. To address this, we introduce a novel space-air-ground integrated network (SAGIN) computing architecture, designed for the efficient offloading of computationintensive applications. Within this architecture, unmanned aerial vehicles (UAVs) conduct edge computing near users, while satellites act as a bridge to cloud computing resources. Given the limitations of UAVs in terms of battery capacity and dynamic network topology, their deployment strategy is crucial for maintaining service quality. Due to the impracticality of collecting global user information for centralized control of UAVs, we have conducted research on the adaptive deployment of UAVs under the condition that they rely solely on local observations. We propose a multi-agent softmax deep double deterministic policy gradient (MASD3) algorithm and comprehensively consider maximizing the uplink transmission rate of terrestrial IoT devices and reducing the energy consumption of UAVs during flight and communication in the optimization objective. Simulation results demonstrate that our proposed solution outperforms existing state-of-the-art baselines.

Task-oriented semantic communication has gained notable interest for its capacity to minimize transmitted data volume without sacrificing task performance. Previous research has mainly concentrated on random masking, which may obscure critical features and hinder the model's ability to learn transferable representations. In this paper, a robust semantic communication system based on semantic-aware masking and discrete codebook (SAMDC) is proposed. Specifically, we develop a semantic-aware sampling strategy, which can selectively mask image patches with low semantic importance instead of random masking, to enhance the model's capacity to learn semantic information and boost training efficiency. Moreover, we also apply an improved robust discrete codebook, shared between the transmitter and receiver. This codebook comprises orthogonal and trainable basis vectors that symbolize the encoded features, thereby enhancing the system's robustness. Experimental results demonstrate that our proposed robust SAMDC significantly enhances the processing efficiency of semantic information. This improvement leads to better performance in communication tasks, particularly in challenging low signal-to-noise ratio (SNR) situations.

The performance benefits of reconfigurable intelligent surfaces (RISs) in enhancing communication capability and coverage are constrained by the static placement of the RIS. However, aerial-RIS, where the RIS is deployed on an unmanned aerial vehicle (UAV), has demonstrated superior potential to improve system performance due to the mobility and adaptability of the UAV. In this paper, we investigate a UAV-mounted RIS (UAV-RIS) relaying system and formulate an optimization problem aimed at maximizing the system sum rate. To address this, we propose a novel algorithm named DL-UTBO, that integrates deep reinforcement learning with a deep neural network (DNN) to jointly optimize the UAV trajectory, base station (BS) active beamforming, and RIS passive beamforming. The simulation results underscore the effectiveness and superiority of the proposed algorithm compared to existing baseline methods.

The integration of semantic communication technology into intelligent vehicular networks represents a promising research direction, as it significantly reduces data transmission volume and spectrum usage, addressing the high demands of transmitting large-scale visual information between vehicles. Existing studies typically assume that communicating parties share a common database. However, this assumption poses significant privacy risks, particularly in inter-vehicle scenarios. To address these challenges, we propose a federated learning-based semantic-aware communication for vehicular networks. In this approach, each intelligent vehicle locally trains a semantic communication model and uploads its model parameters to the edge server for aggregation. To further enhance data transmission efficiency, we introduce a semantic-aware vector quantized variational autoencoder (SVQ-VAE) architecture as the local semantic communication model. This architecture optimizes transmission by selectively compressing and quantizing only the most relevant semantic information for the task. Additionally, to address data heterogeneity among vehicles, we propose a hypernetwork-based personalized federated learning (HPFL) scheme. This approach enhances the model’s scalability and generalization by training a hypernetwork at the edge server to generate specific weight parameters for each vehicle’s semantic communication model. Simulation experiments on the CIFAR-10 and BDD100K datasets demonstrate that our proposed federated learning-based semantic-aware communication achieves superior task completion rates, semantic transmission efficiency and transmission delay compared to existing federated semantic communication architectures.

In recent years, semantic communication has garnered significant attention for its potential to address challenges in traditional communication systems. However, in complex communication environments, semantic communication still faces challenges such as semantic information loss, low transmission efficiency, and poor adaptability. This paper proposes a novel Semantic Communication with Adaptive Semantic Reconstruction (SCASR) scheme to enhance transmission efficiency and adaptability in complex communication environments. First, a compression mechanism based on semantic importance is designed to achieve flexible and efficient semantic compression. Then, we develop an adaptive semantic reconstruction network to predict and reconstruct lost semantic information. Finally, we integrate an attention mechanism into the reconstruction network, dynamically adjusting parameter weights based on Signal-to-Noise Ratio (SNR), Semantic Compression Rate (SCR), and Packet Loss Rate (PLR) to improve reconstruction quality and adaptability. To evaluate the efficiency of SCASR, we conduct extensive simulation experiments on semantic segmentation tasks using the Cityscapes dataset. Results demonstrate that SCASR outperforms existing semantic communication and traditional schemes, offering higher Mean Intersection over Union (mIoU), and enhanced Semantic Transmission Benefit (STB).

In the evolution of the Internet of vehicles (IoV), the increasing demand for vehicular computation tasks presents significant challenges, particularly in the context of constrained local computation resources and high processing delays. To mitigate these challenges, multi-access edge computing (MEC) offers a potential solution by leveraging edge servers for lowlatency processing. However, it also encounters issues such as sub-channel competition and workload imbalance owing to the uneven distribution of vehicle densities. This paper introduces a novel IoV architecture that incorporates multi-task and multi-roadside unit (RSU) capabilities, enabling edge-toedge collaboration for efficient task offloading among RSUs. The optimization problem is formulated with the objective of minimizing the overall task delay, which is further divided into two sub-problems: communication resource allocation and load balancing. Considering the non-deterministic polynomial (NP)- hard nature of these sub-problems, we propose a two-stage deep reinforcement learning-based communication resource allocation and load balancing (DRLCL) algorithm to address them sequentially. Based on realistic vehicle trajectories, comprehensive evaluation results demonstrate the superiority of the proposed algorithm in reducing system delay compared to existing stateof-the-art baselines, offering an effective approach for optimizing the performance of vehicular edge computing (VEC) networks.

In urban environments, direct communication links between a base station (BS) and user equipment (UEs) are often obstructed by buildings. To mitigate these blockages, we integrate unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance system flexibility and improve transmission efficiency. This paper investigates an RIS-assisted multi-user multiple-input single-output (MU-MISO) downlink system, where the RIS is mounted on a UAV. To maximize the system rate while minimizing the UAV's energy consumption and flight duration, we formulate a multi-objective optimization problem. To address this problem, we propose a hybrid algorithm that integrates the soft deep deterministic policy gradient (SD3) algorithm with a graph neural network (GNN) architecture, named SD3-GNN-RIS. The original problem is decomposed into two subproblems: joint active beamforming at the BS and passive beamforming at the RIS, optimized via a GNN-based approach, and three-dimensional (3D) UAV trajectory optimization, formulated as a Markov decision process and solved using the SD3 algorithm. Simulation results demonstrate the superior performance of the proposed algorithm compared to baseline methods in terms of system rate, energy efficiency, and UAV trajectory optimization.

This paper presents an intelligent reflecting surface (IRS)-assisted data harvesting scheme for unmanned aerial vehicle (UAV) networks. This scheme leverages the high maneuverability of the UAV and the channel gain enhancement from the IRS. By jointly optimizing the UAV trajectory and the IRS phase shift, we aim to minimize the completion time of data harvesting missions. Specifically, we devise a softmax operator applicable to deterministic policy gradients and propose a softmax deep double deterministic policy gradients (SD3) method to facilitate the design of three-dimensional trajectory for UAV. In addition, we propose a practical coherent combining (CC) strategy for IRS phase control. Simulation results demonstrate that the proposed SD3-CC algorithm surpasses other mainstream baseline methods relying on deep reinforcement learning (DRL).

Unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) systems are considered a promising solution, especially in disaster scenarios. Furthermore, recon-figurable intelligent surfaces (RISs) have emerged as a novel technology aimed at enhancing the wireless propagation environment in wireless networks. This paper proposes a multi-UAV assisted MEC system with partial task offloading, leveraging RIS to augment the communication performance between ground terminals (GTs) and UAVs. The pre-set hovering positions for the UAVs are determined by clustering the GTs using the K- means algorithm. To minimize system delay and ensure fairness among GTs, the computation offloading strategy and phase-shift of the RIS are optimized using the multi-agent deep deterministic policy gradient (MADDPG) algorithm, a form of multi-agent deep reinforcement learning. Simulation results demonstrate that the proposed cluster-aided RIS-MADDPG algorithm significantly enhances the system delay and fairness performance of the RIS-assisted multi-UAV MEC system when compared to benchmark solutions.

Unmanned aerial vehicles (UAVs) are regarded as a promising solution for mobile edge computing (MEC) systems due to their flexibility and capability to provide computing services to ground terminals (GTs). By leveraging UAVs, the latency in computation tasks can be reduced significantly, particularly in disaster scenarios. Additionally, Reconfigurable Intelligent Surfaces (RIS) have emerged as a novel technology for enhancing the wireless propagation environment in wireless networks. This paper proposes a multi-UAV assisted MEC system where computation tasks of GTs can be computed locally or partially offloaded to UAVs. Furthermore, practical RIS phase shift designs are considered to enhance the communication performance between GTs and UAVs. To minimize the system delay and achieve fairness among GTs, the computation offloading strategy, trajectory of the UAVs are optimized using a markov decision process. Simultaneously, the RIS phase shift is optimized through an alternating optimization algorithm. Additionally, a cooperative multi-agent deep reinforcement learning framework is developed to obtain a optimal solution by employing the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm. Numerical results indicate that MATD3 can effectively improve the system delay and fairness performance of the RIS-assisted multi-UAV MEC system, as compared to benchmark solutions.

Over the past decade, unmanned aerial vehicles (UAVs) have been widely applied in many areas, such as goods delivery, disaster monitoring, search and rescue etc. In most of these applications, autonomous navigation is one of the key techniques that enable UAV to perform various tasks. However, UAV autonomous navigation in complex environments presents significant challenges due to the difficulty in simultaneously observing, orientation, decision and action. In this work, an efficient state-decomposition deep deterministic policy gradient algorithm is proposed for UAV autonomous navigation (SDDPG-NAV) in 3-D complex environments. In SDDPG-NAV, a novel state-decomposition method that uses two subnetworks for the perception-related and target-related states separately is developed to establish more appropriate actor networks. We also designed some objective-oriented reward functions to solve the sparse reward problem, including approaching the target, and avoiding obstacles and step award functions. Moreover, some training strategies are introduced to maintain the balance between exploration and exploitation, and the network is well trained with numerous experiments. The proposed SDDPG-NAV algorithm is capable of adapting to surrounding environments with generalized training experiences and effectively improves UAV’s navigation performance in 3-D complex environments. Comparing with the benchmark DDPG and TD3 algorithms, SDDPG-NAV exhibits better performance in terms of convergence rate, navigation performance, and generalization capability.

The collaboration between unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) presents an innovative approach for delay-tolerant data harvesting in distributed Internet of Things (IoT) networks. However, existing research mostly overlooks the dynamic changes in communication links caused by the real-time UAV movement and the realistic geographical features. In this paper, we address these challenges by considering a practical three-dimensional (3D) urban scenario with a centralized IRS. Our aim is to minimize the completion time of data harvesting missions by jointly optimizing the 3D trajectory of the UAV and the phase shift of the IRS. Specifically, the formulated problem is decoupled into two subproblems. First, for the 3D continuous trajectory design, we propose a robust memory-based softmax deep double deterministic policy gradients (MSD3) approach, which enables the UAV to adaptively collect delay-tolerant data from randomly distributed ground devices starting from any arbitrary point. Second, we present a comprehensive theoretical analysis for the continuous IRS phase control, which provides a practical and intuitive numerical solution. Simulation results demonstrate that the proposed MSD3-IRS algorithm outperforms other mainstream baselines based on deep reinforcement learning.

To face the challenges in emergency scenarios, a multitask and multiobjective optimization algorithm for computation offloading and relay communication is investigated for the air-ground integrated networks, composed of unmanned aerial vehicles (UAVs), emergency vehicle users (EVUs) and ground sensor nodes (GSNs). We propose an HFL-DDQN algorithm, which combines horizontal federated learning (HFL) with double deep$Q$-network (DDQN). First, UAVs are separated into two clusters according to the services they provide, i.e., edge computing or relay communication. Next, the optimization problems are formulated for two types of services, respectively. For the computation offloading tasks of EVUs, the optimization objective is to minimize the weighted sum of delay and energy consumption. For the sensor data transmission of GSNs, the optimization objective is to maximize the minimum rate of relay links. We define the total cost of the system as the sum of two types of services. Then, federated aggregation is used to joint training the global neural networks model without sharing raw data. Furthermore, the DDQN is improved by adopting prioritized experience replay to achieve better convergence. The simulation results show that the proposed HFL-DDQN algorithm not only outperforms the state-of-the-art baselines in terms of the system cost but also promotes the generalization in execution process, which is especially applicable to the rescue scene under accidents.

With the development of Internet of Things (IoT), wireless communication networks and Artificial Intelligence (AI), more and more real-time applications such as online games and autonomous driving have emerged. However, due to limited computing power and battery capacity, it has become increasingly difficult for local user devices to take on the full range of computing tasks under tight timing constraints. The emerging Mobile Edge Computing (MEC) technology is widely considered to be an important technology for achieving ultra-low latency. However, most of the existing work is focused on non-splittable computation tasks. In fact, data partitioning-oriented applications can be split into multiple subtasks for parallel processing. In this paper, we study the partial computation offloading of multiple detachable tasks in MEC networks, focusing on minimizing the total user device latency in the multi-MEC multi-user scenarios. Considering the dynamic partitioning of tasks, we adopt the barrel theory to construct a linear system of equations to find the optimal solutions and propose an approach for distributed computation offloading based on numerical methods. The simulation results show that the proposed algorithm can reduce the average user device latency by 31 % compared with the binary offloading method.

Abstract In the internet of vehicles (IoV) networks, a direct connection from the source end to the destination end may not be established due to the fast vehicle speed, long distance between vehicles, variable vehicle density, serious channel fading etc. In this paper, a joint resource allocation (RA) in the relay‐aided IoV networks is modelled as a mixed binary integer non‐linear programming (MBINP), which maximises the throughput of cognitive IoV networks among different subcarriers and relays. To further reduce the computational complexity, a suboptimal scheme is presented. First, the appropriate relay and subcarrier pairs are obtained by averaging the power allocation among the cognitive sources and relays. Second, an alternative optimisation mechanism is proposed to the power allocation. Simulation results show that, different from the symmetric time‐slot relay transmission, the asymmetric one can significantly increase the degree of freedom for transmission. Therefore, it is more robust to the impact of the relay node location on the throughput. Moreover, the proposed suboptimal RA algorithm not only can obtain the system capacity close to the optimal one, but also can reduce the computational complexity. At the same time, unacceptable degradation caused by severe channel fading is avoided.

In this paper, the resource allocation for vehicle-to-everything (V2X) underlaying 5G cellular mobile communication networks is considered. The optimization problem is modeled as a mixed binary integer nonlinear programming (MBINP), which minimizes the interference to 5G cellular users (CUs) subject to the quality of service (QoS), the total available power, the interference threshold, and the minimal transmission rate. To achieve that, the original MBINP is decomposed into three steps: transmission power initialization, subchannel assignment, and power allocation. Firstly, the minimum transmission power required by the V2X users (VUs) is set as the initial power value. Secondly, the Hungarian algorithm is used to obtain the appropriate subchannel. Finally, an optimization mechanism is proposed to the power allocation. Simulation results show that the proposed algorithm can not only ensure the minimal transmission rate of VUs but also further improve the CUs’ channel capacity under the premise of guaranteeing the QoS of the CUs.

In this paper, a joint video scheduling, subchannel assignment, and power allocation problem in cognitive heterogeneous networks are modeled as a mixed integer non-linear programming (MINLP), which maximizes the minimum video transmission quality among different secondary mobile terminals (MTs) subject to the total available energy at each secondary, the total interference power at each primary base station, the total available capacity at each radio interface of each secondary MTs, and the video sequence encoding characteristic. In order to solve it, we decompose the original MINLP as joint subchannel and power allocation problem and video packet scheduling problem. Then, we model the joint subchannel and power allocation problem as a max-min fractional programming, and transform it as a convex optimization problem. Finally, we utilize dual decomposition method to design a joint subchannel and power allocation algorithm, and propose a video packet scheduling scheme based on auction theory to maximize the video quality for each secondary MT. Simulation results demonstrate that the proposed framework not only improves the video transmission quality significantly, but also guarantees the fairness among different secondary MTs.