IP network slicing can satisfactorily meet the differentiated Service Level Agreement (SLA) requirements of users. However, the current hard slicing in IP networks has the problem of coarse granularity, which restricts the provision of slicing services. In order to address the issue that hard slicing in IP networks cannot be applied on a large scale, we propose a fine-grained hard slicing method based on the semantic encoding of IPv6 source addresses, which is named Source Address Fine-Grained Slicing (SFS). SFS conducts end-to-end scheduling of the underlying physical resources of router devices through the slicing control plane, and then correctly imports the data of users in the slices into the corresponding slicing channels via the slicing forwarding plane. This method refines the slicing granularity and can solve the problem of the inability to apply slicing on a large scale. Finally, we construct an experimental testbed for the intercommunication of SFS and evaluate SFS by using five evaluation indicators. The results show that this method has good compatibility and high reliability. The slicing granularity can reach the Mbps level, and the number of specifications can reach the scale of thousands.

The rapid growth of mobile-based global live streaming services has intensified the challenge of delivering high-quality, real-time video transmission, particularly due to the mismatch between diverse network requirements and rigid resource adaptation strategies. Traditional multipath transmission approaches often lack the dynamic adaptability needed to support service diversity, with isolated path selection leading to resource contention and static or coarse-grained allocation mechanisms failing to respond promptly to network changes. These limitations negatively impact both service quality and overall resource utilization. To address these challenges, this paper presents a flexible real-time service transmission system. Specifically, the system introduces a service diversity-aware mechanism coupled with collaborative optimization of network resource paths, enabling high-quality differentiated transmission services. Furthermore, a dynamic routing optimization algorithm is proposed, leveraging In-band Network Telemetry (INT) technology to monitor network resources in real time and flexibly generate optimal routing strategies. We conducted long-distance (over 2000 kilometers) cross-domain testing in real-world large-scale Information Service Providers (ISPs) networks. Experimental results demonstrate that our solution can operate effectively in existing solutions while outperforming current approaches in terms of Quality of Service (QoS).

Integrated Sensing and Communication (ISAC) introduces greater challenges to network transmission in terms of delay, bandwidth, and reliability. Achieving stable and efficient congestion control is a critical issue in emerging ISAC scenarios. However, many traditional congestion control algorithms primarily focus on transmission efficiency but perform poorly in ensuring fairness between different data flows. To address this limitation, this paper proposes a queuing-aware fair congestion control (Q-FCC) solution for ISAC. In particular, Q-FCC incorporates a queue status monitoring module which can provide real-time feedback on the queuing delays at targeted network switches. Additionally, this paper analyzes the traditional BBR algorithm and identifies an inherent flaw: longer RTT flows have a higher bandwidth gain coefficient compared to shorter RTT flows, leading to unfairness. Based on this insight, Q-FCC introduces bandwidth gain factor. Q-FCC uses the queue status monitoring module to categorize data flows into three types and interacts with bursty flow endpoints via ACK packets to assist them in calculating the bandwidth gain factor, which enables the control of the transmission rate. Finally, the algorithm was implemented in the Linux kernel. The results of the semi-physical simulation show that Q-FCC outperforms the traditional BBR and CUBIC algorithms in terms of bandwidth fairness and transmission stability, respectively.

With the proliferation of Internet of Things (IoT) devices and the exponential growth of data generated at the network edge, there is a pressing need for efficient task offloading strategies in edge-cloud collaborative systems. In this study, we address the optimization of task offloading decisions and computation resource allocation in a multi-user computation resource network comprising edge servers and a centralized cloud server interconnected. Our objective is to minimize both time delay and energy consumption. We formulate the problem as an optimization task aiming to minimize the integrated cost of latency and energy consumption while satisfying the delay and computation resource requirements, resulting in a non-convex, NP-hard problem. To tackle this challenge, we propose a deep reinforcement learning approach, specifically the Actor-Critic based Task Offloading Optimization Network (ACTOON). Extensive simulations are conducted to demonstrate the superiority of ACTOON over other baseline methods.

Recently, live streaming technology has been widely utilized in areas such as online gaming, e‐healthcare, and video conferencing. The increasing network and computational resources required for live streaming increase the cost of content providers and Internet Service Providers (ISPs), which may lead to increased latency or even unavailability of live streaming services. The current research primarily focuses on providing high‐quality services by assessing the resource status of network nodes individually. However, the role assignment within nodes and the interconnectivity among nodes are often overlooked. To fill this gap, we propose a hierarchical game theory‐based live video transmission framework to coordinate the heterogeneity of live tasks and nodes and to improve the resource utilization of nodes and the service satisfaction of users. Secondly, the service node roles are set as producers who are closer to the live streaming source and provide content, consumers who are closer to the end users and process data, and silent nodes who do not participate in the service process, and a non‐cooperative game‐based role competition algorithm is designed to improve the node resource utilization. Furthermore, a matching‐based optimal path algorithm for media services is designed to establish optimal matching associations among service nodes to optimize the service experience. Finally, extensive simulation experiments show that our approach performs better in terms of service latency and bandwidth.

As a key enabling technology in intelligent heterogeneous Internet of Things (IoT), edge caching provides important support for reducing core network load and improving network service efficiency, especially for high bandwidth demand services represented by multimedia applications. However, external time-varying information is hard to be obtained comprehensively in a complicated heterogeneous IoT environment. Meanwhile, there exists the substitutability of content (e.g., videos with different bitrates), which is difficult to make caching decisions online in real-time to achieve fast feedback with low latency and avoid useless deployment. To this end, this article designs a transcoding-enabled online cache scheme for IoT video service with cloud–edge–terminal collaboration. First, we design a variable bitrate video routing strategy to dynamically retrieve content from cloud/edge according to user demands. Furthermore, the video caching problem is considered as an online convex optimization problem to learn utility gradient and determine the optimal caching strategy in real-time without any prior information. On this basis, we extend the problem to elastic networks with dynamic available resources and prove the sublinear regret and sublinear constraint violation. Finally, we summarized five video request data sets and carried out differentiated multiple verifications based on different request habits and content requirements. Compared with the most advanced algorithms in terms of delay, we evaluated the performance advantages of the proposed scheme.

Fairness-aware federated learning (FFL) plays a crucial role in mitigating bias against specific demographic groups (e.g., gender, race, occupation) during collaborative training. Along with the ever-emerging new attack paradigms like gradient leakage and model poisoning, the reliability of FFL also obtains lots of research attention. Either UAV nodes or FFL aggregators could be untrusted adversaries. Although multiple security mechanisms involving encryption, obfuscation, Byzantine-robustness, and detection have been proposed, concrete to UAV networks, the majority of existing solutions are unfeasible due to high heterogeneity and limited resources among participants. Hence, in this paper, we propose mutually reliable FFL (MR-FFL), a stratified community-based framework to facilitate privacy protection (FFL aggregator’s reliability) and poisoning elimination (client nodes’ reliability) jointly for FFL in heterogeneous UAV networks. We first divide UAV nodes into both peer communities (PC) and colleague communities (CC) according to cross-participant similarity and task-oriented fitness, respectively. Thus, the arbitrarily settled learning tasks following fair principles can be efficiently completed by fine-tuned colleague communities, even in the presence of a large degree of heterogeneity among peer communities. Then, we integrate community-specific differential privacy into the MR-FFL process, to achieve privacy amplification as well as efficient and personal collaborative training at the same time. More importantly, we proposed a community-based credit evaluation to resist poisoning attacks in heterogeneous environments. The results on several standard datasets also highlight the performance of MR-Fed in terms of fairness, accuracy, and integrity jointly.

In the past IPTV CDN servers test of China telecom, the testers only conducted stress tests on a single device. The test model only takes into account the factors based on the characteristics of user concurrent traffic, not the end-to-end model and the replacement of video popularity based on user behavior. Therefore, the test results do not accurately reflect the performance of CDN devices in the real environment. According to the current network IPTV CDN networking architecture and service requirements, this paper customized the IPTV CDN service performance index architecture, and designed the end-toend IPTV CDN test bed. At the same time, based on the in-depth analysis and research of the IPTV user access log of a province and the change rule of video heat, this paper designs a user access model with a long-time stability, proposes the intelligent replacement algorithm of content heat, and deploys it in the IPTV CDN test bed. The high-fidelity IPTV CDN test bed proposed in this paper has been officially launched by China Telecom, and has undertaken the evaluation and test of many commercial CDN equipment, as the technical basis of CDN equipment procurement of China Telecom.

As 5G services continue to rapidly develop, operators are experiencing an increasing proportion of network traffic usage devoted to streaming content. Streaming media is particularly suited for transmission in multicast networks. This paper presents a low-latency live streaming system that leverages Bit Index Explicit Replication (BIER) multicast. Furthermore, we complete end-to-end push-pull stream verification within a BIER multicast laboratory to demonstrate the effectiveness of our design. To serve as a case study for this system, we deployed it within an enterprise by connecting internet streaming media to the system. This deployment resulted in significant reduction of resource consumption during internal meetings and training sessions, which were previously using external bandwidth. Additionally, this approach allowed for unified management of streaming media gateways across multiple branches of the enterprise, even those scattered across different geographic regions, optimizing resource allocation. Thus, the system now supports daily operations of internal meetings and training sessions.

Live video streaming services have experienced significant growth, and it has imposed more stringent requirements on current media delivery networks. However, traditional media distribution methods have difficulty meeting the low latency and high bandwidth requirements of emerging live-streaming services. Therefore, for the live video service scenario, we propose a video computing and network convergence (VNCN) model containing multiple nodes and multiple users, which integrates the network and computing resource occupancy of each node in the system and the impact of the media delivery method on the live service. In addition, we also propose a resource-aware dynamic scheduling (RDS) algorithm, which dynamically schedules user requests based on the resources of each node to maximize user Quality of Service (QoS). Finally, the experimental results show that, compared with the commonly used Round-robin (RR) algorithm and the K-Nearest Neighbor (KNN) algorithm, our system can not only provide users with a high QoS live streaming service but also ensure a more balanced load on the resources of each node.

Albeit the popularity of federated learning (FL), recently emerging model-inversion and poisoning attacks arouse extensive concerns towards privacy or model integrity, which catalyzes the developments of secure federated learning (SFL) methods. Nonetheless, the collisions between its privacy and integrity, two equally crucial elements in collaborative learning scenarios, are relatively underexplored. Individuals’ wish to “hide in the crowd” for privacy frequently clashes with aggregators’ need to resist abnormal participants for integrity (i.e., the incompatibility between Byzantine robustness and differential privacy). The dilemma prompts researchers to reflect on how to build mutual confidence between individuals and aggregators. Against the backdrop, this paper proposes a multi-shuffler secure federated learning (MSFL) framework, based on which we further propound three modules (hierarchical shuffling mechanism, malice evaluation module, and composite defense strategy) to jointly guarantee strong privacy protection, efficient poisoning resistance, and agile adversary elimination. Extensive experiments on standard datasets exhibited the method's effectiveness in thwarting different FL poisoning attack paradigms with a minimal cost of privacy breaches.

With the rapid development of virtual reality (VR) technology and the increasing demand for higher video quality, streaming videos imposes stringent requirements on the network. To solve these problems, the Field of View (FOV) streaming is one the popular solutions. In streaming aspect, it's common to provide video service over Internet Protocol Television (IPTV) platform. The application of 8K VR FOV in IPTV platform can reuse the existing Content Delivery Network (CDN) architecture and set-top boxes (STBs), thus significantly reducing the complexity of the system. Furthermore, it can also accelerate the popularization of VR technology, and improve the service development of IPTV platform. Currently, most of the VR FOV solutions in industry are based on Dynamic Adaptive Streaming over HTTP (DASH), which are difficult to apply in most IPTV platforms. Our Real Time Streaming Protocol (RTSP) 8K VR FOV video end-to-end solution for IPTV platforms significantly reduces bandwidth by about 60% compared to the traditional solution, and provides the same subjective quality. We evaluate and compare the performance of our solution with traditional 8K solution. The results show the superiority of the proposed architecture in terms of bandwidth, latency and CPU utilization.

IPTV services deployed nowadays often consist of both live TV and Video-on-Demand (VoD), offered by the same service provider to the same pool of users over the same managed network. Understanding user behaviors in such a setting is hence an important step for system modelling and optimization. Previous studies on user behavior on video services were on either live TV or VoD. For the first time, we conduct an in-depth large-scale behavior study for IPTV users offering simultaneously live TV and VoD choices at the same time. Our data is from the largest IPTV service provider in China, offering hundreds of live channels and hundreds of thousands of VoD files, with traces covering more than 1.9 million users over a period of 5 months. This large dataset provides us a unique opportunity to cross-compare user viewing behaviors for these services on the same platform, and sheds valuable insights on how users interact with such a simultaneous system. Our results lead to new understanding on IPTV user behaviors which have strong implications on system design. For example, we find that the average holding time for VoD is significantly longer than live TV. live TV users tend to surf more. However, if such channel surfing is discounted, the holding times of both services are not much different. While users in VoD tend to view HD longer, channel popularity for live TV is much less dependent on its video quality. In contrast to some popular assumptions on user interactivity, the transitions among live TV, VoD, and offline modes are far from a Markov model.