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Enhanced VR Experience with Edge Computing: The Impact of Decoding Latency

IEEE Transactions on Mobile Computing 2025年第7期24卷 6275-6292页

作者： Huang, Liang Li, Yuqi Liang, Hongyuan Chi, Kaikai Wu, Yuan Zhejiang University of Technology School of Computer Science and Technology China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Macau China

Virtual reality (VR) applications have revolutionized digital interaction by providing immersive experiences.360° VR video streaming has experienced significant growth and popularity as a pivotal VR application. However, the combination of limited network bandwidth and the demand for high-quality videos frequently hinders the achievement of a satisfactory quality of experience (QoE). Although prior methods have enhanced QoE, the effects of decoding latency have been poorly studied. It is technically challenging to design a quality adaptation algorithm that can balance the pursuit of high-quality videos and the limitation of limited bandwidth resources. To address this challenge, we propose an edge-end architecture for 360° VR video streaming and aim to enhance overall QoE by solving a performance optimization problem. Specifically, our experiments on commercial mobile devices in real-world situations reveal that decoding latency significantly influences QoE. First, decoding latency plays a major role in contributing to end-to-end latency, which exceeds the transmission latency. Second, decoding latency can differ considerably between devices with varying computational capabilities. Building on this insight, we propose a novel latency-aware quality adaptation (LAQA) algorithm. LAQA lies in developing a solution that can allocate video quality in real-time and enhance overall QoE. LAQA involves not only the quality of the received content, the transmission latency and the quality variance, but also the decoding latency and the fairness of the user quality. Subsequently, we formulate a combinatorial optimization problem to maximize overall QoE. Through extensive validation with experimental data from real-world situations, LAQA offers a promising approach to enhance QoE and ensure fairness performance in different devices. In particular, LAQA achieves 16.77% and 10.66% enhancement over the state-of-the-art combinatorial optimization and reinforcement learning algorithm

关键词： Bandwidth

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New Offloading Method of Computing Task Based on Gray Wolf Hunting Optimization Mechanism for the IOV

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2264-2277页

作者： Zhang, Jie Zhang, De-Gan Qiao, Meng Hong-Lin, E. Zhang, Ting Zhang, Ping University of Macau Key Laboratory of Internet of Things for Smart City Faculty of Science and Technology China Tianjin University of Technology Tianjin Key Laboratory of Intelligent Computing and Novel Software Technology Tianjin300384 China Tianjin University of Sport School of Sports Economics and Management Tianjin301617 China

Task offloading, as an effective solution, provides low latency and sufficient computing resources for mobile users in the network. However, how to reasonably offload to reduce system overhead is a challenging issue today. This article takes user terminals, edge servers, and idle vehicles with resources as the network structure, and is inspired by the highly social nature of the gray wolf pack. It proposes a new offloading method of edge computing task based on hunting optimizing mechanism of gray wolf for the Internet of Vehicle (IOV). Firstly, an adaptive weight factor is proposed to balance the weight ratio of delay and energy consumption in the system cost under the constraints of delay and energy consumption. With delay and computing resources of vehicles and servers as constraints, a multi constraint minimization system cost problem is proposed. Secondly, the hunting process of the gray Wolf optimization algorithm is used to find the optimal solution of the unloading scheme, The Levy flight strategy was added to enhance the global search ability of the algorithm, and a dynamic weight strategy was introduced to improve the convergence performance of the algorithm. Finally, the improved gray Wolf optimization algorithm was used to solve the optimal unloading plan and minimum cost. The simulation results show that compared with traditional gray Wolf optimization algorithm offloading schemes, the proposed scheme in this paper requires lower system costs. © 2004-2012 IEEE.

关键词： Mobile edge computing

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Low-Carbon Geographically Distributed Cloud-Edge Task Scheduling 24th

Low-Carbon Geographically Distributed Cloud-Edge Task Schedu...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhu, Yingjie Qi, Ji Wang, Zehao Wei, Shengjie Chen, Yan Cao, Tuo Luo, Gangyi Qian, Zhuzhong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Software Technology Co. Ltd. Jiangsu Suzhou China

ISBN: (纸本)9789819615506

Edge computing is a rapidly developing research area known for its ability to reduce latency and improve energy efficiency, and it also has a potential for green computing. Many geographically distributed edge servers are powered by renewable energy sources, due to the difficulties of using traditional power supplies or because of advancements in energy harvesting technologies. These green edge servers can cut down carbon emissions by processing tasks locally, but the inherent limitations of their computing capacity result in some tasks having to be uploaded to a data center to meet service-level agreement (SLA) requirements. To further reduce carbon emissions in cloud-edge systems, scheduling tasks to those low-carbon data centers while meeting latency constraints is highly beneficial. In this paper, we propose a low-carbon cloud-edge scheduling algorithm that utilizes Lyapunov optimization techniques and Markov approximation to address the long-term optimization problem of carbon emissions. Our algorithm guarantees provable performance, and simulation results demonstrate its effectiveness in striking a balance between carbon emissions and task latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon sequestration

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ASR: Efficient and Adaptive Stochastic Resonance for Weak Signal Detection 42

ASR: Efficient and Adaptive Stochastic Resonance for Weak Si...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Chen, Xingyu Liu, Jia Zhang, Xu Chen, Lijun Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

Weak-signal detection underlies a variety of ubiquitous computing applications, such as wireless sensing and machinery fault diagnosis. Stochastic resonance (SR) provides a new way for weak-signal detection by boosting undetectable signals with added white noise. However, existing work has to take a long time to search optimal parameter settings for SR, which cannot fit well some time-critical applications. In this paper, we propose an adaptive SR scheme (ASR) that can amplify the original signal at a low cost in time. The basic idea is that we find that the potential parameter is a key factor that determines the performance of SR. By treating the system as a feedback loop, we can dynamically adjust the potential parameters according to the output signals and make SR happen adaptively. ASR answered two technical questions: how can we evaluate the output signal and how can we tune the potential parameters quickly towards the optimal. In ASR, we first design a spectral-analysis based solution to examine whether SR happens using continuous wavelet transform. After that, we reduce the parameter tuning problem to a constrained non-linear optimization problem and use the sequential quadratic programming to iteratively optimize the potential parameters. We implement ASR and apply it in two ubiquitous computing applications: respiration-rate detection and machinery fault diagnosis. Extensive experiments show that ASR outperforms the state-of-the-art. © 2023 IEEE.

关键词： Spectrum analysis

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mmMIC: Multi-modal Speech Recognition based on mmWave Radar 42

mmMIC: Multi-modal Speech Recognition based on mmWave Radar

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Fan, Long Xie, Lei Lu, Xinran Li, Yi Wang, Chuyu Lu, Sanglu Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

With the proliferation of voice assistants, microphone-based speech recognition technology usually cannot achieve good performance in the situation of multiple sound sources and ambient noises. In this paper, we propose a novel mmWave-based solution to perform speech recognition to tackle the issues of multiple sound sources and ambient noises, by precisely extracting the multi-modal features from lip motion and vocal-cords vibration from the single channel of mmWave. We propose a difference-based method for feature extraction of lip motion to suppress the dynamic interference from body motion and head motion. We propose a speech detection method based on cross-validation of lip motion and vocal-cords vibration so as to avoid wasting computing resources on nonspeaking activities. We propose a multi-modal fusion framework for speech recognition by fusing the signal features from lip motion and vocal-cords vibration with the attention mechanism. We implemented a prototype system and evaluated the performance in real test-beds. Experiment results show that the average speech recognition accuracy is 92.8% in realistic environments. © 2023 IEEE.

关键词： Speech recognition

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AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization 42

AGO: Boosting Mobile AI Inference Performance by Removing Co...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Xu, Zhiying Peng, Hongding Wang, Wei Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

Traditional deep learning compilers rely on heuristics for subgraph generation, which impose extra constraints on graph optimization, e.g., each subgraph can only contain at most one complex operator. In this paper, we propose AGO, a framework for graph optimization with arbitrary structures to boost the inference performance of deep models by removing such constraints. To create new optimization opportunities for complicated subgraphs, we propose intensive operator fusion, which effectively stitches multiple complex operators together for better performance. Further, we design a graph partitioning scheme that allows an arbitrary structure for each subgraph while guaranteeing the acyclic property among all generated subgraphs. Additionally, to enable efficient performance tuning for complicated subgraphs, we devise a divide-and-conquer tuning mechanism to orchestrate different system components. Through extensive experiments on various neural networks and mobile devices, we show that our system can improve the inference performance by up to 3.3× when compared with state-of-the-art vendor libraries and deep compilers. © 2023 IEEE.

关键词： Deep learning

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mmEavesdropper: Signal Augmentation-based Directional Eavesdropping with mmWave Radar 42

mmEavesdropper: Signal Augmentation-based Directional Eavesd...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Feng, Yiwen Zhang, Kai Wang, Chuyu Xie, Lei Ning, Jingyi Chen, Shijia Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

With the popularity of online meetings equipped with speakers, voice privacy security has drawn increasing attention because eavesdropping on the speakers can quickly obtain sensitive information. In this paper, we propose mmEavesdropper, a mmWave based eavesdropping system, which focuses on augmenting the micro-vibration signal via theoretical models for voice recovery. Particularly, to augment the receiving signal of the target vibration, we propose to use beam-forming to facilitate the directional augmentation by suppressing other orientations and use Chirp-Z transform to facilitate the distance augmentation by increasing the range resolution compared with traditional FFT. To augment the vibration signal in the IQ plane, we build a theoretical model to analyze the distortion and propose a segmentation-based fitting method to calibrate the vibration signal. To augment the spectrum for sound recovery, we propose to combine multiple channels and leverage an encoder-decoder based neural network to reconstruct the spectrogram for voice recovery. We perform extensive experiments on mmEavesdropper and the results show that mmEavesdropper can reach the accuracy of 93% on digit and letter recognition. Moreover, mmEavesdropper can reconstruct the voice with an average SNR of 5dB and peak SNR of 17dB. © 2023 IEEE.

关键词： Recovery

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DenseNet-based RFID Grouping Protocols 23

DenseNet-based RFID Grouping Protocols

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2023 ACM Turing Award Celebration Conference, CHINA 2023

作者： Wang, Tianyu Liu, Jia State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9798400702334

The grouping protocol in RFID systems is to label tags according to a given partition so that tags in the identical group hold the same group ID, which makes multi-cast transmissions or aggregate queries possible and thereby improves time efficiency. Existing grouping protocols need to inform each tag individually or each group by transmitting extra filter vectors, which suffer degraded performance. In this paper, we propose a DenseNet-based grouping protocol that labels tags concurrently. The basic idea is to treat the grouping problem as a classification task. The partition information known by an RFID reader is used to train data and obtain a classifier, of which parameters can be broadcast to all tags simultaneously. Each tag can calculate its own group ID according to the parameters. Experimental results demonstrate that our grouping protocol can reduce the execution time by 53% in a real-world RFID system with about 100,000 tags, compared with the state-of-The-Art. © 2023 Owner/Author.

关键词： Efficiency

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A GCN-based Model for Recommendation Using Local Differential Privacy Method 25

A GCN-based Model for Recommendation Using Local Differentia...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Cheng Zhang, Guoming Liu, Bowen Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350330014

Differential privacy (DP) aims to protect user privacy in the age of ubiquitous data collection through inserting perturbations and achieves great success in the area of recommender systems. However, current recommendation models based on differential privacy treat all users equally, but some users want stronger privacy-preserving while some users pay less attention on privacy and want more accurate recommendations. To overcome the above challenge, this paper proposes a new security recommendation model based on graph convolutional network. It allows the users to decide the level of privacy-preserving by them-selves. The proposed model utilizes a personalized-oriented local differential privacy during collecting user data to protect user data. Then a clustering denoising module rebuilds the connections between users and items by deleting unreliable feedback. In this way, users can adjust their level of privacy-preserving locally and meanwhile, the server can still provide accurate personalized recommendations to those who prefer better recommendation performance. This work conducts comprehensive experiments on several real-world benchmark datasets and the experimental results validate the effectiveness of the proposed model. © 2023 IEEE.

关键词： Recommender systems

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On the Fine-Grained Query Complexity of Symmetric Functions 34

On the Fine-Grained Query Complexity of Symmetric Functions

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34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Podder, Supartha Yao, Penghui Ye, Zekun Department of Computer Science Stony Brook University New YorkNY United States State Key Laboratory for Novel Software Technology Nanjing University China Hefei National Laboratory China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9783959772891

Watrous conjectured that the randomized and quantum query complexities of symmetric functions are polynomially equivalent, which was resolved by Ambainis and Aaronson [1], and was later improved in [15, 12]. This paper explores a fine-grained version of the Watrous conjecture, including the randomized and quantum algorithms with success probabilities arbitrarily close to 1/2. Our contributions include the following: 1. An analysis of the optimal success probability of quantum and randomized query algorithms of two fundamental partial symmetric Boolean functions given a fixed number of queries. We prove that for any quantum algorithm computing these two functions using T queries, there exist randomized algorithms using poly(T) queries that achieve the same success probability as the quantum algorithm, even if the success probability is arbitrarily close to 1/2. These two classes of functions are instrumental in analyzing general symmetric functions. 2. We establish that for any total symmetric Boolean function f, if a quantum algorithm uses T queries to compute f with success probability 1/2 + β, then there exists a randomized algorithm using O(T2) queries to compute f with success probability 1/2 + Ω (δβ2) on a 1 − δ fraction of inputs, where β, δ can be arbitrarily small positive values. As a corollary, we prove a randomized version of Aaronson-Ambainis Conjecture [1] for total symmetric Boolean functions in the regime where the success probability of algorithms can be arbitrarily close to 1/2. 3. We present polynomial equivalences for several fundamental complexity measures of partial symmetric Boolean functions. Specifically, we first prove that for certain partial symmetric Boolean functions, quantum query complexity is at most quadratic in approximate degree for any error arbitrarily close to 1/2. Next, we show exact quantum query complexity is at most quadratic in degree. Additionally, we give the tight bounds of several complexity measures, indicating their p

关键词： Quantum theory

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