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Lingjun Pu

Publications
35
Citations
1,011
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (64)
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Lingjun Pu

Affiliation

College of Computer Science
the Key Laboratory of DISSec
the Institute of Systems and Networks, Nankai University, Tianjin, China

Publication Topics

Biography

Lingjun Pu received the PhD degree from Nankai University, in 2016. He was a joint PhD student with the University of Göttingen, Germany, from 2013 to 2015. He is an associate professor with Nankai University, Tianjin, China. His current research interest includes programmable networks, edge intelligence, UHD video streaming, and resource scheduling.(Based on document published on 27 August 2024).
Publications
35
Citations
1,011
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (64)
Author's Published Works

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Towards Neural Codec-Empowered 360^\circ Video Streaming: A Saliency-Aided Synergistic Approach

Jianxin Shi;Miao Zhang;Linfeng Shen;Jiangchuan Liu;Lingjun Pu;Jingdong Xu

IEEE Transactions on Multimedia
Year: 2025 | Volume: 27 | Journal Article |
Networked 360$^\circ$ video has become increasingly popular. Despite the immersive experience for users, its sheer data volume, even with the latest H.266 coding and viewport adaptation, remains a significant challenge to today's networks. Recent studies have shown that integrating deep learning into video coding can significantly enhance compression efficiency, providing new opportunities for hig...Show More

Towards Neural Codec-Empowered 360$^\circ$ Video Streaming: A Saliency-Aided Synergistic Approach

Jianxin Shi;Miao Zhang;Linfeng Shen;Jiangchuan Liu;Lingjun Pu;Jingdong Xu

Year: 2025 | Volume: 27 | Journal Article |

TailClip: Mitigating Tail Latency in Cloud Gaming via Smart Video Frame Generation

Qianyun Gong;Kunheng Jiang;Jingjing Wen;Xinjing Yuan;Jianxin Shi;Lingjun Pu

2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Latency is one of the most significant issues in cloud gaming, among which tail latency, mainly attributed to dynamic network environments (i.e., transmission) and limited device computing capacity (e.g., decoding), has attracted increasing attention. To mitigate the tail latency, different from existing researches considering resource adaptation such as bitrate adaptation, we propose TailClip, wh...Show More

TailClip: Mitigating Tail Latency in Cloud Gaming via Smart Video Frame Generation

Qianyun Gong;Kunheng Jiang;Jingjing Wen;Xinjing Yuan;Jianxin Shi;Lingjun Pu

Year: 2024 | Conference Paper |

${\sf NetDPI}$NetDPI: Efficient Deep Packet Inspection via Filtering-Plus-Verification in Programmable 5G Data Plane for Multi-Access Edge Computing

Chengjin Zhou;Qiao Xiang;Lingjun Pu;Zheli Liu;Yuan Zhang;Xinjing Yuan;Jingdong Xu

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 12 | Journal Article |
In this paper, we advocate ${\sf NetDPI}$NetDPI, a novel and efficient Deep Packet Inspection (DPI) solution built-in 5G Data Plane for multi-access edge computing, leveraging the unique forwarding while computing capability of emerging programmable switches. As the cornerstone, we propose ${\sf FIVE}$FIVE, the first Filtering-plus-Verification algorithm tailored to programmable switches to achiev...Show More

${\sf NetDPI}$NetDPI: Efficient Deep Packet Inspection via Filtering-Plus-Verification in Programmable 5G Data Plane for Multi-Access Edge Computing

Chengjin Zhou;Qiao Xiang;Lingjun Pu;Zheli Liu;Yuan Zhang;Xinjing Yuan;Jingdong Xu

Year: 2024 | Volume: 23, Issue: 12 | Journal Article |

To Distill or Not to Distill: Toward Fast, Accurate, and Communication-Efficient Federated Distillation Learning

Yuan Zhang;Wenlong Zhang;Lingjun Pu;Tao Lin;Jinyao Yan

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 6 | Journal Article |
Cited by: Papers (3)
Apart from the promising potential, federated learning (FL) faces challenges, such as high communication costs and client heterogeneity. Although numerous works have been proposed to address these issues, they lack a holistic perspective to balance all requirements. Moreover, these solutions have not fully utilized the underlying computation capability and network resources, resulting in suboptima...Show More

To Distill or Not to Distill: Toward Fast, Accurate, and Communication-Efficient Federated Distillation Learning

Yuan Zhang;Wenlong Zhang;Lingjun Pu;Tao Lin;Jinyao Yan

Year: 2024 | Volume: 11, Issue: 6 | Journal Article |

EMS: Erasure-Coded Multi-Source Streaming for UHD Videos Within Cloud Native 5G Networks

Lingjun Pu;Jianxin Shi;Xinjing Yuan;Xu Chen;Lei Jiao;Tian Zhang;Jingdong Xu

IEEE Transactions on Mobile Computing
Year: 2024 | Volume: 23, Issue: 2 | Journal Article |
Cited by: Papers (3)
Ultra-High-Definition (UHD) videos have been getting increasing attention. However, existing video streaming solutions fail to deliver them due to the extremely high bandwidth requirement. The emerging cloud native 5G networks have opened up the possibility of enhancing UHD video quality by leveraging in-network video streaming. Unfortunately, the restricted storage and bandwidth of in-network ser...Show More

EMS: Erasure-Coded Multi-Source Streaming for UHD Videos Within Cloud Native 5G Networks

Lingjun Pu;Jianxin Shi;Xinjing Yuan;Xu Chen;Lei Jiao;Tian Zhang;Jingdong Xu

Year: 2024 | Volume: 23, Issue: 2 | Journal Article |

Orchestrating Blockchain with Decentralized Federated Learning in Edge Networks

Yibo Jin;Lei Jiao;Zhuzhong Qian;Ruiting Zhou;Lingjun Pu

2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Decentralized federated learning across edge networks can leverage blockchain with consensus mechanisms for training information exchange among participants over costly and distrustful wide-area networks. However, it is non-trivial to optimally operate the blockchain to support decentralized federated learning due to the complex cost structure of blockchain operations, the balance between blockcha...Show More

Orchestrating Blockchain with Decentralized Federated Learning in Edge Networks

Yibo Jin;Lei Jiao;Zhuzhong Qian;Ruiting Zhou;Lingjun Pu

Year: 2023 | Conference Paper |

When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework

Xinjing Yuan;Lingjun Pu;Lei Jiao;Xiaofei Wang;Meijuan Yang;Jingdong Xu

2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility wi...Show More

When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework

Xinjing Yuan;Lingjun Pu;Lei Jiao;Xiaofei Wang;Meijuan Yang;Jingdong Xu

Year: 2023 | Conference Paper |

QoE-Oriented Mobile Virtual Reality Game in Distributed Edge Networks

Yuan Zhang;Lingjun Pu;Tao Lin;Jinyao Yan

IEEE Transactions on Multimedia
Year: 2023 | Volume: 25 | Journal Article |
Cited by: Papers (3)
Mobile edge computing is a promising framework for mobile virtual reality (VR) game. Although there are several existing studies on the edge assisted mobile VR game system, they lack the consideration of provisioning services with satisfactory QoE to a large number of users. In this paper, we consider the problem of providing QoE-oriented edge assisted mobile VR game as a service to multiple users...Show More

QoE-Oriented Mobile Virtual Reality Game in Distributed Edge Networks

Yuan Zhang;Lingjun Pu;Tao Lin;Jinyao Yan

Year: 2023 | Volume: 25 | Journal Article |

Muster: Multi-Source Streaming for Tile-Based 360° Videos Within Cloud Native 5G Networks

Xinjing Yuan;Lingjun Pu;Jianxin Shi;Qianyun Gong;Jingdong Xu

IEEE Transactions on Mobile Computing
Year: 2023 | Volume: 22, Issue: 11 | Journal Article |
Cited by: Papers (2)
360$^\circ$∘ videos generally require a large amount of bandwidth between video servers and users, which puts much burden on the current CDN-based single-source video streaming solutions. The emerging cloud native 5G networks can bridge the distance between video servers and users by leveraging in-network single-source video streaming to enhance 360$^\circ$∘ video quality. Unfortunately, the restr...Show More

Muster: Multi-Source Streaming for Tile-Based 360° Videos Within Cloud Native 5G Networks

Xinjing Yuan;Lingjun Pu;Jianxin Shi;Qianyun Gong;Jingdong Xu

Year: 2023 | Volume: 22, Issue: 11 | Journal Article |

Adaptive Progressive Image Enhancement for Edge-Assisted Mobile Vision

Daipeng Feng;Liekang Zeng;Lingjun Pu;Xu Chen

2022 18th International Conference on Mobility, Sensing and Networking (MSN)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Recent advances in deep learning models have pushed Super-Resolution (SR) techniques to an unprecedented altitude, enabling high-quality image rendering with variable scaling size and natural fidelity. To deploy them on resource-constrained mobile devices, however, confronts significant chal-lenges of excessively long latency and poor user experience. To this end, we propose Apie, an edge-assisted...Show More

Adaptive Progressive Image Enhancement for Edge-Assisted Mobile Vision

Daipeng Feng;Liekang Zeng;Lingjun Pu;Xu Chen

Year: 2022 | Conference Paper |

Cost-Efficient and Skew-Aware Data Scheduling for Incremental Learning in 5G Networks

Lingjun Pu;Xinjing Yuan;Xiaohang Xu;Xu Chen;Pan Zhou;Jingdong Xu

IEEE Journal on Selected Areas in Communications
Year: 2022 | Volume: 40, Issue: 2 | Journal Article |
Cited by: Papers (3)
To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recognized the power of machine learning (ML) and started to explore its potential to facilitate those network functions. Nevertheless, the current ML models for network functions are often derived in an offline manner, which i...Show More

Cost-Efficient and Skew-Aware Data Scheduling for Incremental Learning in 5G Networks

Lingjun Pu;Xinjing Yuan;Xiaohang Xu;Xu Chen;Pan Zhou;Jingdong Xu

Year: 2022 | Volume: 40, Issue: 2 | Journal Article |

Reinforcement Learning-Based Resource Partitioning for Improving Responsiveness in Cloud Gaming

Yusen Li;Xiwei Wang;Haoyuan Liu;Lingjun Pu;Shanjiang Tang;Gang Wang;Xiaoguang Liu

IEEE Transactions on Computers
Year: 2022 | Volume: 71, Issue: 5 | Journal Article |
Cited by: Papers (12)
Cloud gaming has been very popular in recent years, but issues relating to maintaining low interaction delay to guarantee satisfactory user experience are still prevalent. We observe that the server-side processing delay in cloud gaming system could be heavily influenced by how the resources are partitioned among processes. However, finding the optimal partitioning policy that minimizes the respon...Show More

Reinforcement Learning-Based Resource Partitioning for Improving Responsiveness in Cloud Gaming

Yusen Li;Xiwei Wang;Haoyuan Liu;Lingjun Pu;Shanjiang Tang;Gang Wang;Xiaoguang Liu

Year: 2022 | Volume: 71, Issue: 5 | Journal Article |

An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction

Shuai Yu;Xu Chen;Shuai Wang;Lingjun Pu;Di Wu

IEEE Transactions on Mobile Computing
Year: 2022 | Volume: 21, Issue: 2 | Journal Article |
Cited by: Papers (22)
Image-based three-dimensional (3D) reconstruction utilizes a set of photos to build 3D model and can be widely used in many emerging applications such as augmented reality (AR) and disaster recovery. Most of existing 3D reconstruction methods require a mobile user to walk around the target area and reconstruct objectives with a hand-held camera, which is inefficient and time-consuming. To meet the...Show More

An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction

Shuai Yu;Xu Chen;Shuai Wang;Lingjun Pu;Di Wu

Year: 2022 | Volume: 21, Issue: 2 | Journal Article |

EC-360: Speeding Up 360° Video Streaming Using Tile-based Online Erasure Coding

Jianxin Shi;Lingjun Pu;Tian Zhang;Jingdong Xu

2021 IEEE Global Communications Conference (GLOBECOM)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
360° video services require extremely high bitrate and frame rate videos for a good immersive experience. Traditional solutions for adaptive bitrate streaming are still limited by currently insufficient and fluctuating bandwidth. Besides, viewpoint-aware or tile-based solutions would lead more rebuffering due to the short viewpoint prediction window. In this paper, we present EC-360, a novel video...Show More

EC-360: Speeding Up 360° Video Streaming Using Tile-based Online Erasure Coding

Jianxin Shi;Lingjun Pu;Tian Zhang;Jingdong Xu

Year: 2021 | Conference Paper |

Explore the Impact of Cellular Resource Allocation on Mobile UHD Video Streaming over 5G UDN

Xinjing Yuan;Lingjun Pu;Xiaohang Xu;Jingdong Xu

2021 IEEE Wireless Communications and Networking Conference (WCNC)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
The incoming 5G cellular network is stepping into a densification era, where various kinds of base stations are densely deployed to provide fruitful mobile services such as video streaming. In order to improve the performance of these mobile services, the way to optimally allocate cellular resources for the network-wide users is a crucial problem. In this paper, we consider the mobile Ultra-High-D...Show More

Explore the Impact of Cellular Resource Allocation on Mobile UHD Video Streaming over 5G UDN

Xinjing Yuan;Lingjun Pu;Xiaohang Xu;Jingdong Xu

Year: 2021 | Conference Paper |

Streaming-Aware Cellular Resource Allocation for UHD Video Streaming over Ultra Dense Network

Xinjing Yuan;Lingjun Pu;Xiaohang Xu;Yuwei Xu;Jingdong Xu

2021 IEEE Wireless Communications and Networking Conference (WCNC)
Year: 2021 | Conference Paper |
Ultra-High-Definition (UHD) videos have absorbed great attention in recent years. However, as they are of significant size, streaming them require an extremely high bandwidth to achieve a good quality of experience, which poses a great challenge on the current cellular networks. Realizing the great potentials of coordinated multi-point joint transmission (JT-CoMP) in 5G Ultra Dense Network, we pro...Show More

Streaming-Aware Cellular Resource Allocation for UHD Video Streaming over Ultra Dense Network

Xinjing Yuan;Lingjun Pu;Xiaohang Xu;Yuwei Xu;Jingdong Xu

Year: 2021 | Conference Paper |

QoE-Aware 3D Video Streaming via Deep Reinforcement Learning in Software Defined Networking Enabled Mobile Edge Computing

Pan Zhou;Yulai Xie;Ben Niu;Lingjun Pu;Zichuan Xu;Hao Jiang;Huawei Huang

IEEE Transactions on Network Science and Engineering
Year: 2021 | Volume: 8, Issue: 1 | Journal Article |
Cited by: Papers (19)
With the advancements of wireless network transmission technology, 2D video is hard to satisfy people's requirement for multimedia. Therefore, the high-definition 3D video that can bring a whole new viewing experience is starting to enter people's vision. However, when a tremendously large number of users play 3D video, it puts enormous computational pressure on the cloud server, which incurs high...Show More

QoE-Aware 3D Video Streaming via Deep Reinforcement Learning in Software Defined Networking Enabled Mobile Edge Computing

Pan Zhou;Yulai Xie;Ben Niu;Lingjun Pu;Zichuan Xu;Hao Jiang;Huawei Huang

Year: 2021 | Volume: 8, Issue: 1 | Journal Article |

QoS Optimization of DNN Serving Systems Based on Per-Request Latency Characteristics

Jianfeng Zhang;Wensheng Zhang;Lingjun Pu;Jingdong Xu

2020 16th International Conference on Mobility, Sensing and Networking (MSN)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
Deep Neural Networks (DNNs) have been extensively applied in a variety of tasks, including image classification, object detection, etc. However, DNNs are computationally expensive, making on-device inference impractical due to limited hardware capabilities and high energy consumption. This paper first incorporates downside risk into characteristics of per-request processing latency for DNN serving...Show More

QoS Optimization of DNN Serving Systems Based on Per-Request Latency Characteristics

Jianfeng Zhang;Wensheng Zhang;Lingjun Pu;Jingdong Xu

Year: 2020 | Conference Paper |

Tile-based Multi-source Adaptive Streaming for 360-degree Ultra-High-Definition Videos

Xinjing Yuan;Lingjun Pu;Ruilin Yun;Jingdong Xu

2020 16th International Conference on Mobility, Sensing and Networking (MSN)
Year: 2020 | Conference Paper |
360° UHD videos have absorbed great attention in recent years. However, as they are of significant size and usually watched from a close range, they require extremely high bandwidth for a good immersive experience, which poses a great challenge on the current single-source adaptive streaming strategies. Realizing the great potentials of tile-based video streaming and pervasive edge services, we ad...Show More

Tile-based Multi-source Adaptive Streaming for 360-degree Ultra-High-Definition Videos

Xinjing Yuan;Lingjun Pu;Ruilin Yun;Jingdong Xu

Year: 2020 | Conference Paper |

Allies: Tile-Based Joint Transcoding, Delivery and Caching of 360° Videos in Edge Cloud Networks

Jianxin Shi;Lingjun Pu;Jingdong Xu

2020 IEEE 13th International Conference on Cloud Computing (CLOUD)
Year: 2020 | Conference Paper |
Cited by: Papers (9)
360° or panoramic video applications have seen booming development and absorbed great attention in recent years. However, as they are of significant size and usually watched from a close distance, they require an extremely higher bandwidth and frame rate for a good immersible experience, which poses a great challenge on mobile networks. Realizing the great potentials of tile-based transcoding, vie...Show More

Allies: Tile-Based Joint Transcoding, Delivery and Caching of 360° Videos in Edge Cloud Networks

Jianxin Shi;Lingjun Pu;Jingdong Xu

Year: 2020 | Conference Paper |

CEFL: Online Admission Control, Data Scheduling, and Accuracy Tuning for Cost-Efficient Federated Learning Across Edge Nodes

Zhi Zhou;Song Yang;Lingjun Pu;Shuai Yu

IEEE Internet of Things Journal
Year: 2020 | Volume: 7, Issue: 10 | Journal Article |
Cited by: Papers (57)
With the proliferation of Internet of Things (IoT), zillions of bytes of data are generated at the network edge, incurring an urgent need to push the frontiers of artificial intelligence (AI) to network edge so as to fully unleash the potential of the IoT big data. To materialize such a vision which is known as edge intelligence, federated learning is emerging as a promising solution to enable edg...Show More

CEFL: Online Admission Control, Data Scheduling, and Accuracy Tuning for Cost-Efficient Federated Learning Across Edge Nodes

Zhi Zhou;Song Yang;Lingjun Pu;Shuai Yu

Year: 2020 | Volume: 7, Issue: 10 | Journal Article |

Dynamic Component Placement and Request Scheduling for IoT Big Data Streaming

Yuan Zhang;Jinyao Yan;Lingjun Pu;Shiyu Chen

IEEE Internet of Things Journal
Year: 2020 | Volume: 7, Issue: 8 | Journal Article |
Cited by: Papers (3)
Internet-of-Things (IoT) big data streaming applications, such as video surveillance and automatic driving, tend to use mobile-edge computing (MEC) infrastructure to enhance their performance and augment their functionalities. Although extensive previous studies have worked on offloading requests to MEC servers, none of them has comprehensively and thoroughly considered the important features of I...Show More

Dynamic Component Placement and Request Scheduling for IoT Big Data Streaming

Yuan Zhang;Jinyao Yan;Lingjun Pu;Shiyu Chen

Year: 2020 | Volume: 7, Issue: 8 | Journal Article |

An SDN-Based QoS Guaranteed Mechanism for Geospatial Flows

Feilong Huang;Jianzhong Zhang;Jingdong Xu;Yiran Shao;Lingjun Pu

2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
As a significant portion of big data, geospatial big data is experiencing extremely rapid growth, which brings tremendous pressure and great challenges on geospatial data servers. To provide better services for users, it is an important approach to enable the transmission of geospatial data with QoS, which mainly focus on bandwidth. Therefore, based on the software-defined network(SDN), this paper...Show More

An SDN-Based QoS Guaranteed Mechanism for Geospatial Flows

Feilong Huang;Jianzhong Zhang;Jingdong Xu;Yiran Shao;Lingjun Pu

Year: 2019 | Conference Paper |

Matryoshka: Joint Resource Scheduling for Cost-Efficient MEC in NGFI-Based C-RAN

Mengyao Sun;Lingjun Pu;Jianzhong Zhang;Jingdong Xu

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
In this paper, we consider MEC in NGFI-based C-RAN, a novel and practical MEC framework to facilitate the emerging mobile applications such as AR/VR and video surveillance. However, it is challenging to implement it in a cost-efficient manner (i.e., optimized operational expenditures and service performance), due to the coupled resource provision, service deployment and workload distribution. To s...Show More

Matryoshka: Joint Resource Scheduling for Cost-Efficient MEC in NGFI-Based C-RAN

Mengyao Sun;Lingjun Pu;Jianzhong Zhang;Jingdong Xu

Year: 2019 | Conference Paper |

Chimera: An Energy-Efficient and Deadline-Aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications

Lingjun Pu;Xu Chen;Guoqiang Mao;Qinyi Xie;Jingdong Xu

IEEE Internet of Things Journal
Year: 2019 | Volume: 6, Issue: 1 | Journal Article |
Cited by: Papers (82)
In this paper, we propose Chimera, a novel hybrid edge computing framework, integrated with the emerging edge cloud radio access network, to augment network-wide vehicle resources for future large-scale vehicular crowdsensing applications, by leveraging a multitude of cooperative vehicles and the virtual machine (VM) pool in the edge cloud via the control of the application manager deployed in the...Show More

Chimera: An Energy-Efficient and Deadline-Aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications

Lingjun Pu;Xu Chen;Guoqiang Mao;Qinyi Xie;Jingdong Xu

Year: 2019 | Volume: 6, Issue: 1 | Journal Article |

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