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Two-Timescale Adaptive Live Video Streaming Transmission Mechanism for Vehicular Networks

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 6823-6828页

作者： Feng, Biqian Feng, Chenyuan Min, Geyong Quek, Tony Q. S. Shanghai Jiao Tong Univ Dept Elect Engn Minhang 200240 Peoples R China EURECOM F-06410 Biot France Univ Exeter Dept Comp Sci Exeter EX4 4QF England Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore 487372 Singapore

This research explores a novel adaptive live video streaming transmission strategy over vehicular networks to solve the conundrum between resource-constrained environment and user demand on high Quality-of-Experience (QoE). With an exquisite design of resource types and channel variations, we propose a two-timescale transmission mechanism which allocates the bitrate and bandwidth for each large-timescale frame based on the statistical knowledge of the channel state information (CSI) and refines the power allocation for each small-timescale slot based on instantaneous CSI. Subsequently, we formulate a QoE maximization problem under the restrictions of finite bitrates, bandwidth and power budget. To solve this problem with low computation complexity, we propose a two-stage online successive convex approximation (TOSCA)-based resource allocation algorithm. Simulation results illustrate the rationality and necessity of the proposed dual-time scale optimization, and the proposed mechanism noticeably outperforms the conventional resource allocation schemes under different power budgets and lane configurations.

关键词： Streaming media Bit rate Resource management Bandwidth Optimization Quality of experience approximation algorithms Vectors Transcoding Real-time systems Live video transmission vehicular networks resource allocation two-timescale optimization

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Simple k-crashing Plan with a Good approximation Ratio 23

Simple k-crashing Plan with a Good Approximation Ratio

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Luo, Ruixi Jin, Kai Ye, Zelin Sun Yat-sen University Shenzhen Campus No. 66 Gongchang Road Guangming District Shenzhen China

A project is considered as an activity-on-edge network (AOE network, which is a directed acyclic graph) N, where each activity/job of the project is an edge. Some jobs must be finished before others can be started, as described by the topology structure of N. It is known that job ji in normal speed would take bi days to be finished after it is started, and hence the (normal) duration of the project N, denoted by d(N), is determined, which equals the length of the critical path (namely, the longest path) of N. To speed up the project, the manager can crash a few jobs (namely, reduce the length of the corresponding edges) by investing extra resources into that job. However, the time for completing ji has a lower bound due to technological limits - it requires at least ai days to be completed. Following the convention, assume that the duration of a job has a linear relation with the extra resources put into this job;equivalently, there is a parameter ci (slope), so that shortening ji by d (0 ≤ d ≤ bi − ai) days costs ci · d resources. Given project N and an integer k ≥ 1, the k-crashing problem asks the minimum cost to speed up the project by k days. In this paper, we present a simple solution with the approximation ratio (equation presented). For simplicity, we focus on the linear case throughout the paper, but our proofs are still correct for the convex case, where shortening an edge becomes more difficult after a previous shortening. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： approximation algorithms

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Sparse Count Data Clustering Using an Exponential approximation to Generalized Dirichlet Multinomial Distributions

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第1期33卷 89-102页

作者： Zamzami, Nuha Bouguila, Nizar Concordia Univ Concordia Inst Informat & Syst Engn CIISE Montreal PQ H3G 1T7 Canada Univ Jeddah Dept Comp Sci & Artificial Intelligence Jeddah 23218 Saudi Arabia

Clustering frequency vectors is a challenging task on large data sets considering its high dimensionality and sparsity nature. Generalized Dirichlet multinomial (GDM) distribution is a competitive generative model for count data in terms of accuracy, yet its parameters estimation process is slow. The exponential-family approximation of the multivariate Polya distribution has shown to be efficient to train and cluster data directly, without dimensionality reduction. In this article, we derive an exponential-family approximation to the GDM distributions, and we call it (EGDM). A mixture model is developed based on the new member of the exponential-family of distributions, and its parameters are learned through the deterministic annealing expectation-maximization (DAEM) approach as a new clustering algorithm for count data. Moreover, we propose to estimate the optimal number of EGDM mixture components based on the minimum message length (MML) criterion. We have conducted a set of empirical experiments, concerning text, image, and video clustering, to evaluate the proposed approach performance. Results show that the new model attains a superior performance, and it is considerably faster than the corresponding method for GDM distributions.

关键词： Computational modeling Data models Visualization approximation algorithms Clustering algorithms Mixture models Count data deterministic annealing expectation-maximization (DAEM) exponential family finite mixture models generalized Dirichlet (GD) model selection

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Weighted Packet Selection for Rechargeable Links in Cryptocurrency Networks: Complexity and approximation 30th

Weighted Packet Selection for Rechargeable Links in Cryptocu...

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30th International Colloquium on Structural Information and Communication Complexity (SIROCCO)

作者： Schmid, Stefan Svoboda, Jakub Yeo, Michelle Tech Univ Berlin Berlin Germany IST Austria Klosterneuburg Austria

ISBN: (纸本)9783031327322;9783031327339

We consider a natural problem dealing with weighted packet selection across a rechargeable link, which e.g., finds applications in cryptocurrency networks. The capacity of a link (u, v) is determined by how much nodes u and v allocate for this link. Specifically, the input is a finite ordered sequence of packets that arrive in both directions along a link. Given (u, v) and a packet of weight x going from u to v, node u can either accept or reject the packet. If u accepts the packet, the capacity on link (u, v) decreases by x. Correspondingly, v's capacity on (u, v) increases by x. If a node rejects the packet, this will entail a cost affinely linear in the weight of the packet. A link is "rechargeable" in the sense that the total capacity of the link has to remain constant, but the allocation of capacity at the ends of the link can depend arbitrarily on the nodes' decisions. The goal is to minimise the sum of the capacity injected into the link and the cost of rejecting packets. We show that the problem is NP-hard, but can be approximated efficiently with a ratio of (1 + epsilon) center dot (1 +root 3) for some arbitrary epsilon > 0.

关键词： network algorithms approximation algorithms complexity cryptocurrencies payment channel networks

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Mixed Grover: A Hybrid Version to Improve Grover's Algorithm for Unstructured Database Search

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON QUANTUM ENGINEERING 2025年第1期6卷 1-13页

作者： Piron, Romain Habibie, Muhammad Idham Goursaud, Claire Univ Lyon INSA Lyon Inria CITI EA 3720 F-69007 Lyon France

In this article, we propose a new strategy to exploit Grover's algorithm for unstructured search problems. We first show that running Grover's routine with a reduced number of iterations but allowing several trials presents a complexity advantage while keeping the same success probability. Then, by a theoretical analysis of the performance, we provide a generic procedure to parameterize the number of iterations k within one shot of Grover's algorithm and the maximum number of trials T, given a targeted success pand the size of the database N. At the end, we highlight that this new approach permits to reduce the computational time by at least 10% for p >= 0.999 independently of the size of the database.

关键词： Signal processing algorithms Databases Search problems Registers Flyback transformers Costs approximation algorithms Qubit Optimization Complexity theory Complexity grover's algorithm hybrid quantum computing unstructured search

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A Branch-and-Price Algorithm for the Urban Aerial Delivery Problem With Energy Constraints

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13269-13285页

作者： Pei, Zhi Liang, Zhaohui Huang, Jiayan Fang, Tao Li, Na Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Peoples R China Shanghai Jiao Tong Univ Dept Ind Engn & Management Shanghai 200240 Peoples R China

In this paper, an urban aerial delivery problem (UADP) is investigated, where the parcel transportation service is accomplished by drones in an urban setting. The aim of the problem is to minimize the total service completion time, by taking into account of the flow balance, the energy consumption, and the response time window. To fully explore the structure of the UADP, a mixed integer linear programming (MILP) model is constructed based on an arc-flow scheme. However, directly handling the UADP with commercial solvers is time consuming. In order to enhance the responsiveness of urban courier services and speed up the solving process, a set-covering model (UADP-SC) is proposed with a linear programming based relaxation. Then a branch-and-price algorithm is designed with pricing accelerating strategies based on heuristics. The computational experiments show that the proposed branch-and-price algorithm outperforms the off-the-shelf commercial solvers in terms of computation efficiency. In the mean time, the proposed algorithm can also serve to obtain optimal battery swapping and path planing decisions in face of the large-scale urban aerial delivery problem with energy constraints.

关键词： Drones Batteries Logistics Routing Heuristic algorithms Computational modeling approximation algorithms Automation Urban areas Energy consumption Drone-based logistics mixed integer programming battery swapping branch-and-price algorithm

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Simplification and Improvement of MMS approximation 32

Simplification and Improvement of MMS Approximation

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32nd International Joint Conference on Artificial Intelligence (IJCAI)

作者： Akrami, Hannaneh Garg, Jugal Sharma, Eklavya Taki, Setareh Max Planck Inst Informat Chengdu Peoples R China Univ Saarland Graduiertenschule Informat Saarbrucken Germany Univ Illinois Chicago IL USA Grubhub Chicago IL USA

ISBN: (纸本)9781956792034

We consider the problem of fairly allocating a set of indivisible goods among n agents with additive valuations, using the popular fairness notion of maximin share (MMS). Since MMS allocations do not always exist, a series of works provided existence and algorithms for approximate MMS allocations. The Garg-Taki algorithm gives the current best approximation factor of ( 3/4 + 1/12n). Most of these results are based on complicated analyses, especially those providing better than 2/3 factor. Moreover, since no tight example is known of the Garg-Taki algorithm, it is unclear if this is the best factor of this approach. In this paper, we significantly simplify the analysis of this algorithm and also improve the existence guarantee to a factor of ( 3/4 + min( 1/36, 3/16n-4)). For small n, this provides a noticeable improvement. Furthermore, we present a tight example of this algorithm, showing that this may be the best factor one can hope for with the current techniques.

关键词： approximation algorithms

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AoI and Energy-Aware Data Collection for IRS-Assisted UAV-IoT Networks Under Jamming

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 12166-12180页

作者： Wang, Peng Liu, Kai Ma, Yaodong Gao, Qiang Beihang Univ Sch Elect Informat Engn Beijing 100191 Peoples R China Beihang Univ State Key Lab CNS ATM Beijing 100191 Peoples R China

With the rapid popularity of autonomous aerial vehicles (AAV)-Internet of Things (IoT) networks, timely and energy-efficient data collection is a critical issue. The strong Line-of-Sight (LoS) communication of UAVs makes them more susceptible to jamming attacks, resulting in increased communication latency and energy consumption (EC). In this article, the problem of jointly minimizing Age of Information (AoI) and EC in data collection under malicious jamming attacks is formulated by optimizing the diagonal phase shift matrix of the intelligent reflective surface (IRS), UAV trajectory, and the transmit powers of IoT devices. The formulated problem is solved by an alternating optimization (AO) scheme. Specifically, we first obtain the closed-form solution of the IRS diagonal phase shift matrix by using the quantitative passive beamforming method. Then, the UAV trajectory is optimized by the variable parameter particle swarm annealing (VPPSA) algorithm, and the suboptimal solution of the transmit powers of IoT devices is obtained by the successive convex approximation (SCA) algorithm. Extensive simulation results demonstrate that the proposed algorithm outperforms the benchmark schemes in terms of AoI and EC.

关键词： Internet of Things Autonomous aerial vehicles Jamming Trajectory Data collection Optimization Ink approximation algorithms Uplink Simulation Age of Information (AoI) anti-jamming data collection energy consumption (EC) intelligent reflecting surface (IRS) unmanned aerial vehicle (UAV)

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Semantic Extraction Model Selection for IoT Devices in Edge-Assisted Semantic Communications

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IEEE COMMUNICATIONS LETTERS 2024年第7期28卷 1733-1737页

作者： Chen, Hong Fang, Fang Wang, Xianbin Western Univ Dept Elect & Comp Engn London ON N6A 3K7 Canada Western Univ Dept Comp Sci London ON N6A 3K7 Canada

Semantic communications offer the potential to alleviate communication loads by exchanging meaningful information. However, semantic extraction (SE) is computation-intensive, posing challenges for resource-constrained Internet of Things (IoT) devices. To address this, leveraging computing resources at the edge servers (ESs) is essential. ESs at the access points can support multiple SE models for uploaded SE tasks, making it crucial to select appropriate SE models based on diverse requirements and limited ES computing resources. In this letter, an SE model selection problem is studied in an edge-assisted semantic network. We aim to maximize the total semantic rate of all tasks under SE delay and accuracy requirements, and maximum ES computing capacity. The formulated NP-hard problem is transformed into a modified Knapsack problem equivalently. The proposed efficient approximation algorithm using dynamic programming can yield a guaranteed near-optimum solution. A key insight is revealed that the parameter epsilon is an important indicator to balance the trade-off between the running time and obtained total semantic rate. Simulation results demonstrate the superior performance of proposed solution.

关键词： Semantics Task analysis Internet of Things Computational modeling approximation algorithms Performance evaluation Delays Semantic communications edge computing semantic extraction model selection diverse requirements

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Goemans-Williamson MAXCUT approximation algorithm on Loihi 23

Goemans-Williamson MAXCUT approximation algorithm on Loihi

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Annual Neuro-Inspired Computational Elements Conference (NICE)

作者： Theilman, Bradley H. Aimone, James B. Sandia Natl Labs Albuquerque NM 87123 USA

ISBN: (纸本)9781450399470

approximation algorithms for computationally complex problems are of significant importance in computing as they provide computational guarantees of obtaining practically useful results for otherwise computationally intractable problems. The demonstration of implementing formal approximation algorithms on spiking neuromorphic hardware is a critical step in establishing that neuromorphic computing can offer cost-effective solutions to significant optimization problems while retaining important computational guarantees on the quality of solutions. Here, we demonstrate that the Loihi platform is capable of effectively implementing the Goemans-Williamson (GW) approximation algorithm for MAXCUT, an NP-hard problem that has applications ranging from VLSI design to network analysis. We show that a Loihi implementation of the approximation step of the GW algorithm obtains equivalent maximum cuts of graphs as conventional algorithms, and we describe how different aspects of architecture precision impacts the algorithm performance.

关键词： approximation algorithms

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