IEEE Transactions on Mobile Computing最新文献_第10页

Average Reliability-Optimal Offloading for Mobile Edge Computing in Low-Latency Industrial IoT Networks 低延迟工业物联网网络中移动边缘计算的平均可靠性优化卸载

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-18 DOI: 10.1109/TMC.2025.3541661

Jie Wang;Yao Zhu;Yulin Hu;M. Cenk Gursoy;Anke Schmeink

{"title":"Average Reliability-Optimal Offloading for Mobile Edge Computing in Low-Latency Industrial IoT Networks","authors":"Jie Wang;Yao Zhu;Yulin Hu;M. Cenk Gursoy;Anke Schmeink","doi":"10.1109/TMC.2025.3541661","DOIUrl":"https://doi.org/10.1109/TMC.2025.3541661","url":null,"abstract":"In this paper, we consider a multi-access mobile edge computing (MEC) network with multiple sensors and one MEC server in industrial Internet of Things networks, where the MEC server provides a joint computation service (in the computation phase) for a set of sub-tasks offloaded by different sensors (in the communication phase). Due to the requirements of low latency and ultra reliability, we utilize finite blocklength information theory to characterize the reliability of the communication phase and exploit extreme value theory to investigate the delay violation probability in the computation phase. Following these characterizations, we derive the average end-to-end error probability of the entire service and provide two average end-to-end reliability-optimal design frameworks considering fixed frames structure and dynamic frames structure, in both of which the goal is to minimize the average end-to-end error probability by optimally allocating the total time length to each frame, as well as allocating each frame length to the communication phase and the computation phase. For the fixed frames structure, the original problem is decomposed, and the joint convexity of the decomposed sub-problems is rigorously proved, and the optimal solutions are obtained by the proposed optimal time allocation algorithm. Moreover, for the dynamic frames structure, we reformulate the optimization problem by introducing an average time constraint. By exploiting Lagrange multipliers, we transform the reformulated optimization problem into a dual problem with strong duality, the solutions of which can be obtained by the proposed time allocation algorithm. Via simulations, we validate the proven convexity and the approximation in our analytical model and evaluate the performance for both fixed frames length structure and dynamic frames length structure.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5888-5902"},"PeriodicalIF":7.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge Computing Networks 边缘计算网络中高效分散联邦学习和激励配置的动态分布式模型压缩

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-18 DOI: 10.1109/TMC.2025.3543295

Alia Asheralieva;Dusit Niyato;Xuetao Wei

{"title":"Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge Computing Networks","authors":"Alia Asheralieva;Dusit Niyato;Xuetao Wei","doi":"10.1109/TMC.2025.3543295","DOIUrl":"https://doi.org/10.1109/TMC.2025.3543295","url":null,"abstract":"We study decentralized federated learning (DFL) in edge computing networks where edge nodes (ENs) collaboratively train their artificial intelligence (AI) models in a serverless manner without sharing local data. We consider the following critical DFL challenges: i) scarce bandwidth resources of ENs; ii) dynamic, heterogeneous edge environment; iii) incentive provisioning and complex tradeoffs between the DFL performance and training costs. To resolve these challenges, we develop a new model compression method where ENs utilize dynamic, non-identical compression rates to improve the communication efficiency of DFL under time-varying, heterogeneous resource constraints. We show that our method can be formulated as a graphical Markov potential game where ENs act as players deciding on their compression factors and the number of data samples used for model updates. Each EN is incentivized to participate in DFL through rewards based on the EN's contribution to training. We prove that our game has a dominant pure-strategy Nash equilibrium (NE) maximizing its potential function and propose a dynamic distributed compression algorithm in which each EN can find its dominant strategy independently. We show that this algorithm converges to the Pareto-optimal NE, representing the most efficient solution of our game enhancing the DFL performance with minimal costs.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6293-6314"},"PeriodicalIF":7.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint View Selection, Multigroup Multicast Beamforming, and DIBR for RIS-Aided Multi-View Videos 联合视图选择，多组多播波束形成，以及ris辅助多视图视频的DIBR

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-18 DOI: 10.1109/TMC.2025.3543297

Chi-Han Lee;De-Nian Yang;Guang-Siang Lee;Chih-Hang Wang;Wanjiun Liao

{"title":"Joint View Selection, Multigroup Multicast Beamforming, and DIBR for RIS-Aided Multi-View Videos","authors":"Chi-Han Lee;De-Nian Yang;Guang-Siang Lee;Chih-Hang Wang;Wanjiun Liao","doi":"10.1109/TMC.2025.3543297","DOIUrl":"https://doi.org/10.1109/TMC.2025.3543297","url":null,"abstract":"The rapid development of multi-view videos (MVV) transmission is an irresistible trend. Concurrently, reconfigurable intelligent surface (RIS)-assisted wireless communication has drawn significant attention. We observe that the view selection based on the base station and the view synthesis based on depth-image-based rendering (DIBR) can effectively reduce power consumption. Therefore, this paper studies the view selection and synthesis for RIS-aided MVV in multigroup multicast beamforming. To deal with this complicated scenario, we investigate a problem, named the joint View selection, Multicast beamforming, and DIBR (JVMD), to minimize the total multicast beamforming power, the view transmission operation power, and view synthesis, subject to quality-of-service (QoS), RIS phase shifts, view selection, and DIBR constraints. Unfortunately, the mathematical model is a complicated mixed discrete-continuous optimization problem. To tackle this challenging problem, we designed an algorithm, named View selection, Beamforming, RIS phase, and DIBR (VBRD) algorithm. First, we deal with the discrete optimization problem of selecting the view. VBRD uses the dual-based approximation methodology to round back a primal's integer solution. Then, in the continuous optimization problem, we apply the alternating optimization (AO) method to determine beamforming, RIS phase, and DIBR. Finally, simulation results show the performance of exploiting view synthesis for RIS-assisted wireless communication.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6376-6393"},"PeriodicalIF":7.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Monitoring Utility of Uncrewed Aerial Vehicles Considering Adverse Effects 考虑不利影响的无人机监测效用优化

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-18 DOI: 10.1109/TMC.2025.3543399

Haihan Zhang;Haipeng Dai;Yu Qiu;Enze Yu;Ruiben Zhou;Weijun Wang;Jingwu Wang;Guihai Chen

{"title":"Optimizing Monitoring Utility of Uncrewed Aerial Vehicles Considering Adverse Effects","authors":"Haihan Zhang;Haipeng Dai;Yu Qiu;Enze Yu;Ruiben Zhou;Weijun Wang;Jingwu Wang;Guihai Chen","doi":"10.1109/TMC.2025.3543399","DOIUrl":"https://doi.org/10.1109/TMC.2025.3543399","url":null,"abstract":"For Unmanned Aerial Vehicles (UAVs) monitoring tasks, capturing high quality images of target objects is important for subsequent recognition. Concerning the problem, many prior works study placement/trajectory planning for UAVs to maximize the quality of captured images. However, all of them overlook a fact that UAV monitoring may cause a huge risk/annoyance on living objects. In this paper, we investigate the novel problem of oPtimizing uncrewed aErial vehicles plAcement by Considering both monitoring utility and adverse Effects (PEACE). We propose an approach to solve PEACE, which is proved to be NP-hard. Overall, our approach achieves a <inline-formula><tex-math>$1- frac{1}{e}-varepsilon$</tex-math></inline-formula> approximation ratio. First, we approximate the original problem of PEACE as a classical problem of Monotone Submodular function Maximization under a Uniform Matroid constraint (MSMUM) with a controlled gap. Then, for MSMUM, we propose a combination of algorithms achieving a <inline-formula><tex-math>$1-frac{1}{e}$</tex-math></inline-formula> approximation and <inline-formula><tex-math>$O(nlog n)$</tex-math></inline-formula> time complexity considering the correlation among the UAV monitoring strategies. The proposed algorithms outperform existing algorithms for MSMUM through theoretical analysis and experimental results. Extensive simulations and field experiments demonstrate the effectiveness of our approach, achieving performance gains of 9.0% to 1434.5% compared to existing methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5996-6013"},"PeriodicalIF":7.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Mobile Energy Replenishment and Data Gathering in Wireless Sensor Networks via Federated Deep Reinforcement Learning 基于联合深度强化学习的无线传感器网络联合移动能量补充和数据采集

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-17 DOI: 10.1109/TMC.2025.3543009

Haojun Huang;Junhao Zhang;Bang Wang;Wang Miao;Geyong Min

{"title":"Joint Mobile Energy Replenishment and Data Gathering in Wireless Sensor Networks via Federated Deep Reinforcement Learning","authors":"Haojun Huang;Junhao Zhang;Bang Wang;Wang Miao;Geyong Min","doi":"10.1109/TMC.2025.3543009","DOIUrl":"https://doi.org/10.1109/TMC.2025.3543009","url":null,"abstract":"Recent years have witnessed the proliferation of wireless energy transfer for Wireless Sensor Networks (WSNs), which are mainly used for data gathering in real-world applications. A number of studies have investigated mobile vehicle scheduling to charge sensor nodes via wireless Mobile Chargers (MCs). Unfortunately, most of them cannot parallelly charge all nodes in an intelligent manner with the global network attributes. Furthermore, the time-variable charging ignores the optimal data gathering, resulting in poor Joint Energy Replenishment and Data Gathering (JERDG). To fill this gap, this paper proposes a Federated Deep Reinforcement Learning (FDRL)-based JERDG (FERG) solution for WSNs. To this end, FERG first partitions the networks into a set of clusters to distribute the workload evenly among multiple MCs, and then designs an FDRL-based framework that incorporates various time-variant network attributes to determine the optimal schedule for charging and data gathering via multiple MCs and a base station (BS). The BS as the cloud server is responsible for global training of JERDG models, while multiple MCs will parallelly train local models to jointly charge energy-exhausted nodes and gather the data from all nodes in clusters. To reserve more personalized characteristics of each cluster, a density-based partial aggregation strategy is designed to train the global model. Furthermore, a reward-weighted update and selection solution is proposed to generate and exploit reference samples with high rewards. Simulation results obtained from various scenarios demonstrate that FERG significantly outperforms the state-of-the-art approaches in terms of network lifetime, energy efficiency and data collection latency.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6460-6473"},"PeriodicalIF":7.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization-Inspired Graph Neural Network for Cellular Network Optimization 面向蜂窝网络优化的优化启发图神经网络

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-17 DOI: 10.1109/TMC.2025.3542434

Pengcheng He;Yijia Tang;Fan Xu;Qingjiang Shi

{"title":"Optimization-Inspired Graph Neural Network for Cellular Network Optimization","authors":"Pengcheng He;Yijia Tang;Fan Xu;Qingjiang Shi","doi":"10.1109/TMC.2025.3542434","DOIUrl":"https://doi.org/10.1109/TMC.2025.3542434","url":null,"abstract":"The rapid development of wireless communications has driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model’s generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model’s applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6446-6459"},"PeriodicalIF":7.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Truthful Online Combinatorial Auction-Based Mechanisms for Task Offloading in Mobile Edge Computing 移动边缘计算中基于诚实在线组合拍卖的任务卸载机制

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-14 DOI: 10.1109/TMC.2025.3542135

Xueyi Wang;Xingwei Wang;Chen Wang;Rongfei Zeng;Lianbo Ma;Qiang He;Min Huang

{"title":"Truthful Online Combinatorial Auction-Based Mechanisms for Task Offloading in Mobile Edge Computing","authors":"Xueyi Wang;Xingwei Wang;Chen Wang;Rongfei Zeng;Lianbo Ma;Qiang He;Min Huang","doi":"10.1109/TMC.2025.3542135","DOIUrl":"https://doi.org/10.1109/TMC.2025.3542135","url":null,"abstract":"Mobile edge computation (MEC) is envisioned as a prospective approach for processing the computation-intensive and delay-sensitive tasks of smart mobile devices (SMDs) through offloading them to base stations (BSs) nearby. In fact, efficient task offloading mechanisms are crucial to accomplish an MEC system. The key challenge is to make on-spot decisions upon the arrival of each task and at the same time achieve truthfulness of each SMD. The challenge further escalates, when the unique characteristics of an MEC system, such as locality constraint, delay constraint, etc., are explicitly considered. To solve the challenge, we present a truthful online combinatorial auction-based mechanism (TOCA) for task offloading in an MEC system. Specifically, we first devise the candidate offloading scheme determination algorithm, aiming to determine the candidate offloading schemes of an SMD upon the arrival of its task. Next, we devise the winning offloading scheme selection and pricing algorithm based on the online primal-dual optimization framework, to decide the winning scheme among the SMD's candidate offloading schemes and calculate its payment. By solid theoretical analysis, we verify that TOCA achieves truthfulness, individual rationality and computational efficiency and a smaller competitive ratio. Trace-driven simulation studies validate the effectiveness and efficacy of TOCA.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6488-6502"},"PeriodicalIF":7.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Physical Layer Authentication Framework Exploiting Array Pattern Feature for mmWave MIMO Systems 利用阵列模式特征的毫米波MIMO系统分布式物理层认证框架

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-13 DOI: 10.1109/TMC.2025.3541725

Pinchang Zhang;Keshuang Han;Yuanyu Zhang;Yulong Shen;Fu Xiao;Xiaohong Jiang

{"title":"Distributed Physical Layer Authentication Framework Exploiting Array Pattern Feature for mmWave MIMO Systems","authors":"Pinchang Zhang;Keshuang Han;Yuanyu Zhang;Yulong Shen;Fu Xiao;Xiaohong Jiang","doi":"10.1109/TMC.2025.3541725","DOIUrl":"https://doi.org/10.1109/TMC.2025.3541725","url":null,"abstract":"Authentication in millimeter-Wave (mmWave) Multiple-Input Multiple-Output (MIMO) systems is a critical issue due to the unique characteristics of mmWave communication, such as highly directional beamforming and the ability to support massive device connectivity. To address this challenge, this paper proposes a novel low-complexity decision-level-based Distributed Physical Layer Authentication (DPLA) framework to combat identity-based impersonation attacks in mmWave MIMO systems. The DPLA framework leverages Beam Pattern (BP) deviation, which arises from hardware-specific gain errors, as a key authentication feature. A fusion center is introduced to make the final authentication decision by aggregating local decisions from multiple collaborative nodes, enabling multi-directional perception. Specifically, a low-complexity hybrid combining fusion rule is carefully designed to accommodate the fully connected structure of mmWave MIMO systems, balancing computational efficiency and authentication performance. A rigorous performance analysis is conducted by deriving closed-form analytical expressions for the probabilities of correct detection and false alarm. Furthermore, the asymptotic detection and discrimination performance are systematically analyzed in the large-scale antenna regime. To further enhance authentication accuracy, digital signaling matrices are designed using the deflection coefficient maximization principle. The feasibility of the proposed framework is validated through a comprehensive evaluation, demonstrating its superior robustness and efficiency compared to benchmark methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6430-6445"},"PeriodicalIF":7.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Symbiotic Resource Pricing in the Computing Continuum Era 计算连续体时代的共生资源定价

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-13 DOI: 10.1109/TMC.2025.3542017

Aisha B Rahman;Panagiotis Charatsaris;Eirini Eleni Tsiropoulou;Symeon Papavassiliou

{"title":"Symbiotic Resource Pricing in the Computing Continuum Era","authors":"Aisha B Rahman;Panagiotis Charatsaris;Eirini Eleni Tsiropoulou;Symeon Papavassiliou","doi":"10.1109/TMC.2025.3542017","DOIUrl":"https://doi.org/10.1109/TMC.2025.3542017","url":null,"abstract":"Though extensive research efforts have been devoted to the problem of computing resource pricing, they mainly focus on single computing paradigms. In this paper, we provide a holistic approach to this problem, by treating the whole computing continuum, consisting of cloud, edge, and fog computing providers, simultaneously offering their resources to the users. Within such a complex setting, we establish the concept of symbiotic computing resource pricing and sharing, where the computing providers and the users coexist within a mutually beneficial ecosystem, sharing services and resources as a means of ensuring their business survival and service satisfaction. Under this prism, we introduce two key pricing families, namely the non-cooperative one which involves competition and is treated through game theoretic approaches, and the cooperative resource pricing (full or partial), which addresses complex scenarios through optimization and coalition. A thorough performance assessment is provided, through modeling and simulation, in order to highlight and quantify the key characteristics and tradeoffs of the various resource pricing approaches introduced.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6474-6487"},"PeriodicalIF":7.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive 360-Degree Streaming: Optimizing With Multi-Window and Stochastic Viewport Prediction 自适应360度流：优化与多窗口和随机视口预测

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-13 DOI: 10.1109/TMC.2025.3541748

Weichao Feng;Shuoyao Wang;Yu Dai

{"title":"Adaptive 360-Degree Streaming: Optimizing With Multi-Window and Stochastic Viewport Prediction","authors":"Weichao Feng;Shuoyao Wang;Yu Dai","doi":"10.1109/TMC.2025.3541748","DOIUrl":"https://doi.org/10.1109/TMC.2025.3541748","url":null,"abstract":"The tile-based approach is widely adopted in adaptive 360-degree video streaming systems, due to its efficiency in managing limited bandwidth resources. Recently, significant research efforts have been devoted to viewport-prediction-enabled bitrate adaptation for tile-based 360-degree Adaptive Bit-Rate (ABR) streaming, towards improving the average video quality while reducing rebuffering. However, the inherent uncertainty of users’ viewports has posed limitations on users’ Quality of Experience (QoE) for tile-based 360-degree ABR streaming. In this paper, we introduce a multi-window and stochastic viewport prediction approach to address the viewport uncertainty. In particular, considering our goal of maximizing the expectation of future QoE, we investigate a viewport distribution prediction model, to cope with the inherent randomness. Additionally, to accommodate the varying gap between the playback and the download process, we explore the multiple-window viewport prediction models to capture different prediction gaps. Even with the utilization of distributional prediction and multi-window models, predicting viewports far into the future is still inherently challenging. Accordingly, we propose a patience pattern temporarily suspending the download process, allowing for the accumulation of additional head movement trajectory data. Finally, we employ a model predictive control (MPC) approach for sequential decision-making, formulating the MPC problem as a mixed-integer non-linear programming (MINLP) task. To mitigate the computational burden associated with solving MINLP, we introduce a mixed-integer linear programming transformation to achieve efficient decision-making. Extensive experiments, utilizing real-world traces and user head movement trajectories, demonstrate that the proposed method outperforms state-of-the-art methods, improving overall QoE performance by 16.75% –18.91% .","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5903-5915"},"PeriodicalIF":7.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0