Efficient Heuristic Algorithm Research Articles

Path Planning for Multi-Vehicle-Assisted Multi-UAVs in Mobile Crowdsensing

Due to the capability of fast deployment and controllable mobility, unmanned aerial vehicles (UAVs) play an important role in mobile crowdsensing (MCS). However, constrained by limited battery capacity, UAVs cannot serve a wide area. In response to this problem, the ground vehicle is introduced and used to transport, release, and recycle UAVs. However, existing works only consider a special scenario: one ground vehicle with multiple UAVs. In this paper, we consider a more general scenario: multiple ground vehicles with multiple UAVs. We formalize the multi-vehicle-assisted multi-UAV path planning problem, which is a joint route planning and task assignment problem (RPTSP). To solve RPTSP, an efficient multi-vehicle-assisted multi-UAV path planning algorithm (MVP) is proposed. In MVP, we first allocate the detecting points to proper parking spots and then propose an efficient heuristic allocation algorithm EHA to plan the paths of ground vehicles. Besides, a genetic algorithm and reinforcement learning are utilized to plan the paths of UAVs. MVP maximizes the profits of an MCS carrier with a response time constraint and minimizes the number of employed vehicles. Finally, performance evaluation demonstrates that MVP outperforms the baseline algorithm.

Sustainable and Robust Home Healthcare Logistics: A Response to the COVID-19 Pandemic

Today, research on healthcare logistics is an important challenge in developing and developed countries, especially when a pandemic such as COVID-19 occurs. The responses required during such a pandemic would benefit from an efficiently designed model for robust and sustainable healthcare logistics. In this study, we focus on home healthcare logistics and services for planning the routing and scheduling of caregivers to visit patients’ homes. Due to the need for social distancing during the COVID-19 pandemic, these services are highly applicable for reducing the growth of the epidemic. In addition to this challenge, home healthcare logistics and services must be redesigned to meet the standards of a triple bottom line approach based on sustainable development goals. A triple bottom line approach finds a balance between economic, environmental, and social criteria for making a sustainable decision. Although, recently, the concept of green home healthcare has been studied based on the total cost and green emissions of home healthcare logistics and services, as far as we know, no research has been conducted on the formulation of a triple bottom line approach for home healthcare logistics and services. To achieve social justice for caregivers, the goal of balancing working time is to find a balance between unemployment time and overtime. Another contribution of this research is to develop a scenario-based robust optimization approach to address the uncertainty of home healthcare logistics and services and to assist with making robust decisions for home healthcare planning. Since our multi-objective optimization model for sustainable and robust home healthcare logistics and services is more complex than other studies, the last novel contribution of this research is to establish an efficient heuristic algorithm based on the Lagrangian relaxation theory. An initial solution is found by defining three heuristic algorithms. Our heuristic algorithms use a symmetric pattern for allocating patients to pharmacies and planning the routing of caregivers. Then, a combination of the epsilon constraint method and the Lagrangian relaxation theory is proposed to generate high-quality Pareto-based solutions in a reasonable time period. Finally, an extensive analysis is done to show that our multi-objective optimization model and proposed heuristic algorithm are efficient and practical, as well as some sensitivities are studied to provide some managerial insights for achieving sustainable and robust home healthcare services in practice.

Scheduling Precedence Constrained Tasks for Mobile Applications in Fog Computing

We consider scheduling precedence constrained tasks of a mobile application in a fog computing environment, which faces multiple challenges of precedence constraints, power allocation, and performance-cost tradeoff. Our strategies to handle the three challenges are described as follows. First, in pre-power-allocation algorithms and post-power-allocation algorithms, precedence constraints are handled by the classic list scheduling algorithm and the level-by-level scheduling method respectively. Second, in a pre-power-allocation algorithm (a post-power-allocation algorithm, respectively), a power allocation strategy is determined before (after, respectively) a computation offloading strategy is decided. Third, the performance-cost tradeoff is dealt with by defining the energy-constrained scheduling problem and the time-constrained scheduling problem. That is, between performance and cost, we fix one and minimize the other. The main contributions of the present paper are highlighted as follows. We develop a class of pre-power-allocation algorithms for both energy-constrained and time-constrained scheduling, which are based on the classic list scheduling algorithm and the equal-energy method. We develop a class of post-power-allocation algorithms for both energy-constrained and time-constrained scheduling, which are based on the level-by-level scheduling method and our previously proposed algorithms for independent tasks. We evaluate the proposed algorithms by extensive experiments on mobile applications with randomly generated directed acyclic graphs and identify the most effective and efficient heuristic algorithms. Our research in this paper studies computation offloading in the context of traditional task scheduling while incorporating new and unique features of fog computing into consideration. To the author’s best knowledge, there has been no such and similar study in the current literature.

Trust-Based Scheduling Framework for Big Data Processing with MapReduce

Security and privacy have become a great concern in cloud computing platforms in which users risk the leakage of their private data. The leakage can happen while the data is at rest (in storage), in processing, or on moving within a cloud or between different cloud infrastructures, e.g., from private to public clouds. This paper focuses on protecting data “in processing”. For big data applications, the MapReduce framework has been proven as an efficient solution and has been widely deployed, e.g., in healthcare and business data analysis. In this article, we propose a trust-based framework for MapReduce in big data processing tasks. Specifically, we first quantify and propose to assign the sensitive values for data and trust values for map and reduce slots. We then compute the trust value of each resource employed in the big data processing tasks. Depending on the data's sensitivity level of a task, the task requires a given level of trust (i.e., higher sensitive data requires servers/slots with higher trust level). The MapReduce scheduling problem is then formulated as the maximum weighted matching problem of a bipartite graph that aims to maximize the total trust value over all possible assignments subject to various trust requirement of different tasks. The problem is known to be NP-hard. To tackle it, we observe that within a computing node (VM), slots share the same trust value granted from the secured transformation phase. This helps reduce the number of slot nodes of a weight bipartite graph. Leveraging this fact, we propose an efficient heuristic algorithm that achieves 94.7 percent of the optimal solution obtained via exhaustive search. Extensive simulations show that the trust-based scheduling scheme provides much higher protection for data sensitivity while ensuring good performance for big data applications.

Active deep learning on entity resolution by risk sampling

While the state-of-the-art performance on entity resolution (ER) has been achieved by deep learning, its effectiveness depends on large quantities of accurately labeled training data. To alleviate the data labeling burden, Active Learning (AL) presents itself as a feasible solution that focuses on data deemed useful for model training.Building upon the recent advances in risk analysis for ER, which can provide a more refined estimate on label misprediction risk than the simpler classifier outputs, we propose a novel AL approach of risk sampling for ER. Risk sampling leverages misprediction risk estimation for active instance selection. Based on the core-set characterization for AL, we theoretically derive an optimization model which aims to minimize core-set loss with non-uniform Lipschitz continuity. Since the defined weighted K-medoids problem is NP-hard, we then present an efficient heuristic algorithm. Finally, we empirically verify the efficacy of the proposed approach on real data by a comparative study. Our extensive experiments have shown that it outperforms the existing alternatives by considerable margins.

The max‐p‐compact‐regions problem

AbstractThe max‐p‐compact‐regions problem involves the aggregation of a set of small areas into an unknown maximum number (p) of compact, homogeneous, and spatially contiguous regions such that a regional attribute value is higher than a predefined threshold. The max‐p‐compact‐regions problem is an extension of the max‐p‐regions problem accounting for compactness. The max‐p‐regions model has been widely used to define study regions in many application cases since it allows users to specify criteria and then to identify a regionalization scheme. However, the max‐p‐regions model does not consider compactness even though compactness is usually a desirable goal in regionalization, implying ideal accessibility and apparent homogeneity. This article discusses how to integrate a compactness measure into the max‐p regionalization process by constructing a multiobjective optimization model that maximizes the number of regions while optimizing the compactness of identified regions. An efficient heuristic algorithm is developed to address the computational intensity of the max‐p‐compact‐regions problem so that it can be applied to large‐scale practical regionalization problems. This new algorithm will be implemented in the open‐source Python Spatial Analysis Library. One hypothetical and one practical application of the max‐p‐compact‐regions problem are introduced to demonstrate the effectiveness and efficiency of the proposed algorithm.

Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles

Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes. We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.

Online crowdsourced truck delivery using historical information

Various crowdsourced logistics platforms are forming rapidly along with the booming mobile Internet. Motivated by modern crowdsourced truck logistics platforms, we introduce the online crowdsourced truck delivery (OCTD) problem and reformulate it as the online bipartite hyper-matching problem. We then explore the possibility of accommodating historical information to design efficient online algorithms to serve online orders. To the best of our knowledge, it is the first work on incorporating historical information to solve the online bipartite hyper-matching problem. Depending on whether orders can be partially served, we investigate two practical situations, i.e. separable and inseparable cases. For the inseparable case, we propose a randomized online algorithm, named Hyper-Matching, whose competitive ratio is a non-decreasing function of the amount of historical information. For the separable case, we modify Hyper-Matching to present another randomized online algorithm, named Separable-Hyper-Matching. It is worth noting that the competitive ratios of Hyper-Matching and Separable-Hyper-Matchingeither beat or match the current best online algorithms when no historical information is considered. We then present four computationally efficient heuristic algorithms, including a greedy variant and a batch-processing variant for each of the inseparable and separable cases. We perform a sequence of experiments using synthetic and real-world datasets, with an emphasis on the influence that historical information has on algorithm performance. The experiment results demonstrate the effectiveness of our algorithms and particularly the positive influence of historical information on our algorithms.

Planned Special Event Network Optimization Model Considering Parking and Ridesharing Drop-Off

A planned special event (PSE), such as a sports game or a concert, can greatly affect the normal operations of a transportation system. To facilitate traffic, the road network is usually reconfigured, which could include road closures, reversed lanes, and limited access to parking facilities. For recurring PSEs, event-goers are often provided with recommended routes to designated parking areas in advance. Such network reconfiguration and route and parking recommendations are, however, often ad hoc in practice. This paper focuses on the PSE traffic planning problem. We propose to simultaneously consider parking, ridesharing, and network configuration. The problem is formulated as an optimization problem with integer decision variables. We developed a flow-based traffic simulation tool that is able to incorporate parking and lane changing (which cannot be ignored around ridesharing drop-off locations) to evaluate the objective function. We also developed effective and efficient heuristic solution algorithms. The models and algorithms are tested using the real network and traffic data from Super Bowl XLIX in 2015. The results show that our methods and approaches are able to produce an effective comprehensive traffic plan with reasonable computation time. For the Super Bowl XLIX case study, the resulting optimal plan is able to save 39.6% of the total vehicle-hours associated with default network configurations. Sensitivity analysis has also been conducted with respect to the compliance rate of travelers following recommended routes. It is found that the resulting near-optimal PSE traffic plans are able to tolerate some uncertainty in the compliance rate.

Attribute‐scale selection for hybrid data with test cost constraint: The approach and uncertainty measures

Recently several novel cost-sensitive attribute-scale selection approaches have been proposed based on measurement errors. They are significant because they can simultaneously select attributes and scale combination to minimize the cost consumed in data processing. However, these approaches cannot deal with hybrid data with test cost constraint, and most of them do not consider the scale diversity between different attributes; and these approaches do not touch the uncertainty measurement, all of which are important issues in real applications. To address this situation, in this paper an effective cost-sensitive attribute-scale selection approach is presented based on the rough set theory, and multiple relevant uncertainty measures are developed. The main contributions of the paper are threefold. First, a generalized confidence level vector-based neighborhood rough set model is constructed. It takes into account the scale diversity between different attributes of hybrid data. Then, multiple uncertainty measures are developed. They consider both attributes and scales, thus are more general than existing ones which consider only attributes or only scales. Finally, an efficient heuristic attribute-scale selection algorithm is designed, which can select attributes and their respective scales to minimize the consumed total cost of hybrid data under any rational value of test cost upper bound. Detailed experiments thoroughly confirm the effectiveness of the proposed cost-sensitive attribute-scale selection approach. The experiments also reveal the influences of different test cost upper bounds to the attribute-scale selection and some related quantities including the uncertainty measures. This study would enrich the rough set theory to some extent, and provide an effective support for some test cost-constrained decision makings.

Cost-minimized virtual elastic optical network provisioning with guaranteed QoS

Network virtualization is important for elastic optical networks (EONs) because of more flexible service provisioning. To ensure guaranteed quality of service (QoS) for each virtual elastic optical network (VEON), clients usually request network resources from a network operator based on their bandwidth requirements predicted from historical traffic demands. However, this may not be efficient as the actual traffic demands of users always fluctuate. To tackle this, we propose a new VEON service provisioning scheme, called SATP, which consists of three stages, i.e., spectrum assignment (SA), spectrum trading (ST), and spectrum purchasing (SP). Unlike conventional once-for-all VEON service provisioning approaches, the SATP scheme first allocates spectrum resources to VEONs according to their predicted bandwidth requirements with a satisfaction ratio α (0 < α < 1). Then, to minimize service degradation on VEONs which are short of assigned spectra for their peak traffic periods, the scheme allows VEONs to trade spectra with each other according to their actual bandwidth requirements. Finally, it allows VEON clients to purchase extra spectrum resources from a network operator if the spectrum resources are still insufficient. To optimize this entire process, we formulate the problem as a mixed integer linear programming (MILP) model and also develop efficient heuristic algorithms for each stage to handle large test scenarios. Simulations are conducted under different test conditions for both static and dynamic traffic demand scenarios. Results show that the proposed SATP scheme is efficient and can achieve significant performance improvement under both static and dynamic scenarios.

Latency-Bounded Off-Site Virtual Node Protection in NFV

In network function virtualization (NFV), the client’s service requests will go through multiple service functions (SFs). The instances of the required SFs will be hosted on the geographically-distributed physical nodes in physical networks (PNs). The failure of any physical nodes or virtual nodes (i.e., SF instances) will impact the delivery of services and the client’s experience. It is essential for service providers to take the node failure and network reliability into account. Different from physical node failures that will affect the PN’s topology and connectivity, virtual node failures impact the services delivery of certain clients. As a result, applying traditional backup schemes that are designed for physical node failures may not efficiently provide protection for virtual node failures. In this work, we define and mathematically formulate a new latency-bounded off-site virtual node protection (LOVNP) problem in NFV. After proving the NP-hardness of the LOVNP problem, we introduce a novel shared protection technique called pairwise node protection to effectively facilitate the protection of node failures in NFV. Then, we propose an efficient heuristic algorithm called protection centrality based pairwise node protection (PC-PNP) to optimize the LOVNP problem and prove that PC-PNP has a logarithm-approximation boundary. Our extensive simulations and analysis show that the proposed algorithm significantly outperforms the algorithms that are extended from the existing work.

Deep Q-Network and Traffic Prediction based Routing Optimization in Software Defined Networks

Software Defined Networking (SDN) is gaining momentum not only in research but also in IT industry representing the drivers of 5G networks, due to its capabilities of increasing the flexibility of a network and address a variety of network challenges, by logically centralizing the intelligence in software-based controllers. Thanks to Machine Learning (ML) techniques, the network performances and utilization can be optimized and enhanced. Neural Networks (NN) and Reinforcement Learning (RL), in particular, have demonstrated great success in cooperating with complex problems arising in network operation and management. To this end, we exploit in this paper, an SDN-based rules placement approach that aims to dynamically predict the traffic congestion by using mainly NN and learn optimal paths and reroute traffic to improve network utilization by deploying a Deep Q-Network (DQN) agent. To this end, we first formulate the Quality-of-Service (QoS)-aware routing problem as a Linear Program (LP), whose objective is to minimize the end-to-end (E2E) delay and link utilization. Then, we propose a simple yet efficient heuristic algorithm to solve it. Numerical results through emulation using ONOS controller and Mininet demonstrate that the proposed approach can significantly improve network performances in terms of decreasing the link utilization, the packet loss and the E2E delay.

A population perturbation and elimination strategy based genetic algorithm for multi-satellite TT&amp;C scheduling problem

The Multi-satellite Tracking Telemetry and Command (TT&C) Scheduling, a multi-constrained and high-conflict complex combinatorial optimization problem, is a typical NP-hard problem. The effective utilization of existing TT&C resources has always played a key role in the satellite field. This paper first simplified the problem and established a corresponding mathematical model with the hybrid objective of maximizing the profit and task completion rate. Considering the significant effect of genetic algorithm in solving the problem of resource allocation, a population perturbation and elimination strategy based genetic algorithm (GA-PE) which focused on the Multi-Satellite TT&C Scheduling problem was proposed. For each case, a task scheduling sequence was first obtained through the GA-PE algorithm, and then a task planning algorithm will be used to determine which tasks can be scheduled. Compared with several efficient heuristic algorithms, a series of computational experiments have illustrated its better performance in both profit and task completion rates. The experiments of strategy and parameter sensitivity verification have investigated the performance of GA-PE in various aspects thoroughly.

Energy Efficient D2D Communications Using Multiple UAV Relays

In this paper, we propose a novel optimization model for multiple Unmanned Aerial Vehicles (UAVs) working simultaneously as relays to help two set of ground users; namely relay users and device-to-device (D2D) users. The relay users are assumed out communication rages from each other and use the UAV link to for their data transmission. While the D2D users are assumed to be nearby users and use the UAV for managing the resources without being involved in the transmission. The goal of the paper is to operate the UAVs in an energy-efficient manner to support the different set of users by i) optimizing the available bandwidth and power allocations of the D2D links, and ii) acting as relays when needed to maintain the communication links between relay users. We formulate an optimization problem that maximizes the throughput-energy utility while respecting the resource availability including the UAVs’ energy consumption, UAV-user association, and trajectory constraints. Due to the non-convexity of the problem, we propose to solve it in three steps using Taylor series approximation. Firstly, we optimize the transmit power of the UAVs and users. Then, we optimize the bandwidth allocation for a given transmit power values. Finally, an efficient heuristic algorithm based on a recursive shrink-and-realign process is proposed to optimize the UAVs’ trajectories. The performance of the proposed method shows advantages in terms of average throughput compared to the fixed power and bandwidth solutions.

Asymmetric CDC ROADMs for Efficient Support of Bi-Directionally Asymmetric Traffic Demands

With the arrival of 5G era, Internet traffic is growing explosively. Meanwhile, the asymmetry of traffic in upstream and downstream directions is becoming more severe. Most current network architectures are designed based on the assumption of bi-directionally symmetric traffic demands. Network capacity in the direction that carries less traffic is largely wasted. To tackle this issue, we propose a new optical network architecture that can efficiently handle bi-directionally asymmetric traffic demands. Specifically, we design a novel asymmetric colorless, directionless, and contentionless (CDC) reconfigurable add/drop multiplexer (ROADM), which can adaptively configure numbers of ingress and egress 1 ×N wavelength selective switches (WSSs) in each direction asymmetrically according to actual traffic demands, thereby avoiding wastage of 1 ×N WSSs in the direction carrying less traffic. Also, we decouple each transponder into a pair of split transmitter (Tx) and receiver (Rx), and asymmetrically configure their numbers according to actual bi-directional traffic demands, significantly reducing the add/drop cost in a ROADM. To evaluate the benefit of the proposed asymmetric network architecture, we exploit the routing and spectrum assignment problem in an optical network. A novel analytical model to evaluate lightpath blocking performance with asymmetric numbers of opposite fibers on each link of an optical network is developed. Also, efficient heuristic algorithms to target asymmetric traffic are proposed. Simulation results show that the proposed asymmetric optical network architecture can achieve significantly better lightpath blocking performance than its counterpart symmetric architecture when their total system costs are close. Furthermore, the analytical model can accurately predict the blocking performance of an optical network based on the asymmetric architecture.

Single-Machine Scheduling with Fixed Periodic Preventive Maintenance to Minimise the Total Weighted Completion Times

The single-machine scheduling problem with fixed periodic preventive maintenance, in which preventive maintenance is implemented periodically to maintain good machine operational status and decrease the cost caused by sudden machine failure, is studied in this paper. The adopted objective function is to minimise the total weighted completion time, which is representative of the minimisation of the global holding/inventory cost in the system. This problem is proven to be NP-hard; a position-based mixed integer programming model and an efficient heuristic algorithm with local improvement strategy are developed for the total weighted completion time problem. To evaluate the performances of the proposed heuristic algorithms, two new lower bounds are further developed. Computational experiments show that the proposed heuristic can rapidly achieve optimal results for small-sized problems and obtain near-optimal solutions with tight average relative percentage deviation for large-sized problems.

Multi-Domain and Multi-Material Topology Optimization in Design and Strengthening of Innovative Sustainable Structures

Expectations and challenges of modern sustainable engineering and architecture stimulate intensive development of structural analysis and design techniques. Designing durable, light and eco-friendly constructions starts at the conceptual stage, where new efficient design and optimization tools need to be implemented. Innovative methods, like topology optimization, become more often a daily practice of engineers and architects in the process of solving more and more demanding up-to-date engineering problems efficiently. Topology optimization is a dynamically developing research area with numerous applications to many research and engineering fields, ranging from the mechanical industry, through civil engineering to architecture. The motivation behind the present study is to make an attempt to broaden the area of topology optimization applications by presenting an original approach regarding the implementation of the multi-domain and multi-material topology optimization to the design and the strengthening/retrofitting of structures. Moreover, the implementation of the design-dependent self-weight loading into the design model is taken into account as a significantly important issue, since it influences the final results of the topology optimization process, especially when considering massive engineering structures. As an optimization tool, the original efficient heuristic algorithm based on Cellular Automata concept is utilized.

Compatible Influence Maximization in Online Social Networks

Influence maximization, which aims to find a small number of influencers in a social network to maximize the influence spread under a certain propagation model, has attracted substantial attention due to its widespread applications, such as viral marketing and social advertising. However, most of the former studies focus primarily on maximizing the influence spread of a single product, which is not very common in actual marketing campaigns. In this article, we study a novel compatible influence maximization problem for two considered products, which involves more complex product adoption decisions of users in many realistic settings. The problem is NP-hard, and the objective function no longer exhibits monotonicity and submodularity. We propose an adapted greedy algorithm to solve the problem effectively. Due to its poor computational efficiency in the seed selection, we further propose a fast greedy algorithm that integrates several effective optimization strategies without compromising the accuracy and devise an efficient heuristic algorithm to approximate the influence spread calculation. Extensive experiments over real-world social networks of different sizes demonstrate the effectiveness and efficiency of the proposed methods.

OSCAR: On Optimizing Resource Utilization in Live Video Streaming

Live video streaming traffic and related applications have experienced significant growth in recent years. However, this has been accompanied by some challenging issues, especially in terms of resource utilization. Although IP multicasting can be recognized as an efficient mechanism to cope with these challenges, it suffers from many problems. Applying software-defined networking (SDN) and network function virtualization (NFV) technologies enable researchers to cope with IP multicasting issues in novel ways. In this article, by leveraging the SDN concept, we introduce OSCAR (Optimizing reSourCe utilizAtion in live video stReaming) as a new cost-aware video streaming approach to provide advanced video coding (AVC)-based live streaming services in the network. In this article, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder function (VTF). At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, by executing a mixed-integer linear program (MILP), the SDN controller determines a group of optimal multicast trees for streaming the requested videos from an appropriate origin server to the VRPs. Moreover, to elevate the efficiency of resource allocation and meet the given end-to-end latency threshold, OSCAR delivers only the highest requested quality from the origin server to an optimal group of VTFs over a multicast tree. The selected VTFs then transcode the received video segments and transmit them to the requesting VRPs in a multicast fashion. To mitigate the time complexity of the proposed MILP model, we present a simple and efficient heuristic algorithm that determines a near-optimal solution in polynomial time. Using the MiniNet emulator, we evaluate the performance of OSCAR in various scenarios. The results show that OSCAR surpasses other SVC- and AVC-based multicast and unicast approaches in terms of cost and resource utilization.