The exponential surge in network traffic has imposed significant challenges on traditional Internet architectures, resulting in high latency and redundant transmissions. Cache-enabled networks alleviate these issues by deploying content closer to end-users, making the planning of such networks a research focus. However, regional heterogeneity in user demand and caching interdependencies among hierarchical nodes complicate the planning process. Most existing approaches rely on simplistic even allocation or empirical methods, which fail to simultaneously meet user performance expectations and minimize deployment costs. This paper proposes a network planning framework based on the Equivalent Characteristic Time Approximation (ECTA). The approach begins by establishing a performance–resource mapping. Using ECTA, we decouple the tightly coupled characteristic time relationships across hierarchical nodes, thereby accurately estimating the required cache capacity and bandwidth needed to achieve user performance targets. Building on this foundation, we formulated the network planning as a constrained convex optimization problem that minimizes deployment cost while satisfying user performance constraints. We conducted extensive experiments on a large-scale simulation platform (ndnSIM) and a real-world cache-enabled network testbed (CENI-HeFei). The results demonstrate that, under identical network topologies and total resource constraints, our method significantly improves cache hit probability while reducing deployment costs compared to homogeneous resource allocation schemes. This work provides a practical theoretical foundation and valuable insights for the design, deployment, and optimization of future cache-enabled networks.

The exponential growth of data creates significant challenges for distributed storage systems. Conventional cache management architectures face limitations in performance and scalability, primarily due to uncoordinated resource utilization across front-end and back-end networks, skewed data access patterns, and the demands of ultra-high concurrency. In this paper, we propose CacheMon, an in-network cache coordination system based on hybrid topology for massively scalable distributed storage. CacheMon establishes a control plane in the programmable switch to integrate front-end and back-end resources, ending the inefficiency of traditionally isolated networks. We design two key mechanisms in CacheMon: a cache tracking mechanism for maintaining data consistency through location recording, and a unified load balancing mechanism that leverages in-network measurements to optimize resource allocation and adaptively counter workload skew. Experimental results confirm that CacheMon significantly improves throughput performance and system scalability under skewed and concurrent workloads, leveraging its hybrid topology to efficiently coordinate network resources.

QoE metrics for video provides network operators with insight into the quality of service of their video delivery, giving them valid information to optimize bandwidth resource allocation. However, with the popularization of end-to-end encryption protocols (e.g., SSL/TLS), operators cannot directly obtain valuable information from encrypted traffic. In this paper, we present MultiQoE, which leverages multimodal features with multihead attention mechanism, enabling more accurate and wide-ranging real-time DASH video QoE measurements. We carefully select round-trip time (RTT) and throughput (THR) as multimodal input features so as to capture complementary information. Building on this, we develop a robust deep learning architecture that integrates convolutional neural network for effective feature extraction and multihead attention mechanism for enhanced contextual understanding. This combination allows the model to process complex relationships between the input modalities and deliver more precise measurement related to video QoE metrics. We evaluate MultiQoE on the real-world DASH traffic dataset collected from our platform, and it outperform existing methods in QoE measurement across four tasks. Resolution and rebuffering time classification improve by 2% and 0.54%, while MSE for rebuffering duration and end time decrease by 4.32% and 1.54%, respectively.

Congestion control is the main method to solve network congestion. The Bottleneck Band-Width and Round-Trip ropagation time(BBR) congestion control algorithm, proposed by Google in 2016, can achieve lower latency while maintaining higher throughput. However, in high-bandwidth, long-delay network conditions, BBR and other improved algorithms suffer from low bandwidth utilization and slow convergence. In order to ameliorate the above problems, the QM_BBR algorithm proposed in this paper, improves the transmission performance of each phase by 1) Improving the speed of the Startup phase based on comparison, 2) Adjusting the performance gain of the ProbeBw phase based on adaptation, 3) Adding a new Quick-Measurement phase based on the state judgment, which can adaptively adjust the pacing gain according to the current network latency and the network congestion to make the network congestion end more quickly. The experimental results show that QM_BBR improves the convergence speed by up to 18%, reduces the retransmission by 77%, and increases the throughput by 8.1% compared with BBR.

Cache-enabled networks present challenges in managing rapidly changing information demand and accommodating diverse user preferences. This paper proposes a cache placement strategy named SMAC. SMAC is specifically tailored for video scenes and comprehensively considers the spatiotemporal characteristics of contents. By deeply analyzing the characteristics of data across three dimensions: platform, style, and theme of videos, SMAC can accurately capture and predict demand patterns. Additionally, SMAC introduces a cache threshold adaptive adjustment mechanism based on a sliding module. The mechanism dynamically adjusts the content placement level of caching according to changes in user preferences over time. Experimental results indicate that, compared to some common strategies, under various experimental conditions, SMAC can increase the hit ratio by 3-8%, reduce server load by 5-30%, and decrease total delay by 3-22%. The demonstration of these network performance validates the effectiveness of the SMAC.

The importance of encrypted traffic classification for network management and security is self-evident. The emergence of programmable data plane (PDP) technology makes it possible to directly implement encrypted traffic classification in the data plane, which can classify network traffics in line-rate. In this paper, we propose a two-phase encrypted traffic classification (TP-ETC) scheme in programmable data plane. In TP-ETC, Convolutional Neural Network (CNN) is employed for classifying highly similar traffic with high accuracy in the first phase, and Long Short-Term Memory (LSTM) model is responsible for classifying all remaining traffic with low storage overhead in the second phase, achieving the best balance between accuracy and storage overhead. We also design a feature extraction method suitable for PDP, effectively reducing the overhead of feature storage. In addition, we design a table segmentation algorithm to reduce the growth rate of table entries to a linear level. The experimental results demonstrate the superiority of the proposed scheme TP-ETC.

Video delivery over wireless networks with limited network resources and dynamically changing channel quality is an important challenge, and one of the most promising solutions for tackling this problem is to employ multicast transmissions, which improves network resource utilization efficiency. This article focuses on delivering video concurrently to multiple users over mobile networks leveraging Multicast Broadcast Multimedia Service (MBMS) and Mobile Edge Computing (MEC) technology. We propose a$k$-means clustering and cooperative bargaining game-based adaptive video multicast solution (KGS) over mobile edge networks, with the goal of providing high-quality video delivery service in an envisaged MBMS service area across multiple cell sites. By taking user subgrouping, resource allocation, and bitrate adaptation into account, we establish a Cooperative Bargaining Game (CBG) based joint optimization model for multiple Multicast Broadcast Synchronized Frequency Network (MBSFN) users in mobile edge networks. Then we transform this model into a two-stage convex optimization problem and a nonlinear integer programming problem. We propose a heuristic approach to solve them and achieve a Pareto optimal video delivery strategy for all users. Finally, the efficiency of the proposed scheme is evaluated through extensive simulations.

The proliferation of 360-degree video applications has brought significant challenges to existing networks. To meet the requirements of high transmission rate, low interaction latency, and high reliability, Mobile Edge Computing (MEC) has emerged as a promising technology that enables caching and processing at network edges. In this article, we present DACOD360, a deadline-aware content delivery system for the 360-degree video streaming over MEC networks. To address the challenges such as unpredictable viewports, uneven cached tiles, concurrent requests, and dynamic bandwidth, we formulate the deadline-aware delivery problem as a long-term integer program model to maximize the Quality of Experience (QoE) under the constraints of network bandwidth, cache capacity, and deadline. This optimization problem is a complex sequential decision that considers both deadline-constrained service quality at the temporal scale and multi-user resource allocation at the spatial scale. To solve it, we decompose the original problem into two sub-problems and solve them iteratively using Deep Reinforcement Learning (DRL) and Cooperative Bargaining Game (CBG). Comprehensive experiments are conducted in a wide variety of environments, and the results demonstrate that our proposed scheme outperforms the state-of-the-art schemes in terms of long-term QoE, traffic reduction, and other metrics.

The rapid growth of mobile network traffic poses major challenges for current wireless networks regarding bandwidth, delay, mobility, and stability. To overcome these obstacles, a new network architecture called Information-Centric Networking (ICN) has emerged, effectively addressing these issues and enhancing content delivery efficiency. However, with the in-network content cache, anyone including unauthorized users can access the content from intermediate network nodes. In response to this challenge, this paper proposes an efficient and lightweight ICN content access control framework based on a hybrid-coding mechanism that combines two or more encoding operations, which does not impose additional complexity on ICN routers. In the proposed scheme, the content is first divided into multiple original blocks, and these original blocks are encoded into encoded blocks using hybrid-coding operations. Each authorized user can obtain private decoding information from the content provider, and decode them into original content using its private decoding information. The proposed scheme can fully utilize ICN’s in-network cache capability and defend against a wide range of attacks. Furthermore, security analysis, ndnSIM-based simulation, and real-world experiments demonstrate the scheme’s security, performance, and scalability.

Variations in wireless network bandwidth will have a significant impact on the performance of mobile live video streaming. When multiple users have different network latency, the way of uploading a higher bitrate version of previously uploaded video segments may improve the quality of experience (QoE) of users with high network latency. In this paper, we propose an upload-download collaborative transmission scheme for mobile live video streaming with the goal of improving the overall QoE of all users. Moreover, we designed a frame-based transmission and scheduling mechanism to reduce the delay experienced by users watching live videos. Then, we design a joint upload-download transmission algorithm based on deep reinforcement learning (DRL) that takes into account the states of both the video upload and download sides. Through extensive simulation in multi-client mobile live video streaming scenarios, the proposed scheme outperforms existing solutions in terms of overall QoE, smoothness, and live video delay.

Information-Centric Networking (ICN) is a commercially viable network architecture that enables content and location separation through in-network caching, reducing dupli-cate traffic and improving network resource utilisation. As with other networks, a reasonable pricing mechanism can facilitate the deployment of ICNs by encouraging operators to participate in the deployment of ICNs. A large number of studies on ICN pricing mechanisms have been conducted in which users pay for traffic on a per-unit basis, as opposed to the real-life method of paying for traffic on packages set by operators. In this paper, based on studying the interaction between users and ISPs and CPs and establishing the utility functions of each role, we analyse the caching and pricing strategies of each entity under NASH equilibrium and compare and analyse which charging model is more able to meet the needs of network entities in the ICN environment. It is found that the package billing model is more in line with the needs of operators and users in I CN s, and can achieve the objective of incentivising ICN development. This paper also contributes to the development of the best pricing strategy for ICN networks.

Information-Centric Networking (ICN) has the characteristics of in-network caching, which can reduce the transmission of duplicate traffic, reduce the load on the servers and improve the user experience. From a technical point of view, it is a very promising network architecture. A reasonable pricing mechanism can encourage internet service providers, content providers and users to participate in the operation and use of ICN, and convert ICN technical advantages into economic benefits, thereby promote the large-scale deployment of ICN. The current research focuses on ICN pricing to analyze the pricing mechanism on the internet service provider (ISP) side and the corresponding market equilibrium results. But the model of content providers (CPs) is usually relatively simple in this research. The model assumes the existence of one single CP operator, which will be very different from future deployment scenarios. Multiple CPs will introduce competition and stimulate end users to use ICN networks and ISPs to deploy ICN networks. Moreover, the relationship between CPs is not only competitive but also cooperative. This paper focuses on the complex relationship of competition and cooperation among multiple CPs, solves the non-cooperative game model based on game theory, and studies the interaction between cache and pricing strategies of ICN entities. The optimal cache share of ISPs and the optimal pricing of ISPs and CPs are obtained by establishing the optimal utility function of each entity. Finally, numerical analysis is performed to derive the utility function of ICN entities as the critical pricing and caching parameters change, while verifying the consistency with the equilibrium solution.

The performance of Dynamic Adaptive Streaming (DAS) in multi-client scenarios can be improved by taking advantage of the aggregation capability of Named Data Networking (NDN). In this paper, we propose a client-side game theory based (GB) ABR algorithm for NDN that can achieve proactive aggregation of requests among clients as much as possible without requiring coordinating with other clients or scheduling by a central controller. We model the interaction between a DAS client and network as an incomplete information non-cooperative game. Then, this game is transformed into a complete but imperfect information game by Harsanyi transformation, and each client can issue an appropriate bitrate request by solving the Bayesian Nash Equilibrium (BNE) problem respectively. By designing the payoff function pair elaborately, the equilibrium point of the game can correspond to the situation that multiple clients issuing the same video bitrate request, that is, requests aggregation, which will reduce the repeated traffic and also achieve fairness. Compared with the existing solutions, through simulation and real-world experiments in multi-client video distribution scenarios, the GB algorithm outperforms the comparison algorithms in terms of overall Quality of Experience (QoE), fairness, and network bandwidth utilization, etc.

Caching contents close to end-users can improve the network performance, while causing the problem of guaranteeing consistency. Specifically, solutions are classified into validation and invalidation, the latter of which can provide strong cache consistency strictly required in some scenarios. To date, little work on the analysis of cache invalidation has been covered. In this work, by using conditional probability to characterize the interactive relationship between existence and validity, we develop an analytical model that evaluates the performance (hit probability and server load) of four different invalidation schemes with LRU replacement under arbitrary invalidation frequency distribution. The model allows us to theoretically identify some key parameters that affect our metrics of interest and gain some common insights on parameter settings to balance the performance of cache invalidation. Compared with other cache invalidation models, our model can achieve higher accuracy in predicting the cache hit probability. We also conduct extensive simulations that demonstrate the achievable performance of our model.

The scenario that quantities of users subscribing to a same live video content cluster together in a spatially local area poses challenges to cellular operators even in 5G unicast networks. In this regard, we propose a network paradigm exploiting eMBMS and edge computing, which relieves resource starvation in both backbone and wired access networks by grouping users and distributing the desired content to each multicast group only once. However, problems are raised by operators to perform an optimal server-side decision: how to partition users with the heterogeneity and dynamic of channel conditions, how to fairly and optimally allot resources considering both unicast and multicast users, and how to maximize the overall QoE of this live video service. To cope with these coupled problems, we formulate an optimization model based on coalition game with QoE assured, which defines a fair allocation strategy according to respective contributions and a dynamic grouping method. Subsequently, we propose a heuristic algorithm with a low-time complexity that guarantees QoE for most users and shows conspicuous reduction of annoying stalling events. Noticeably, numerical simulations reveal the fairness and near-optimality of our algorithm compared with state-of-the-art approaches in multiple scenarios.

Current schemes for Dynamic Adaptive Streaming over HTTP (DASH) are mainly client-driven. Thus, in the scenario of multiple users watching the same video, repeated subscription and data transmission results in an under-utilization of network bandwidth resources. Additionally, competition for limited network resources of individual users may motivate selfish behaviors, which leads to unfairness and sub-optimal utility of video services. In this paper, Multimedia Broadcast Multicast Service (MBMS) in mobile edge networks for multi-bitrate video sessions is applied to overcome these limitations. We formulate a non-linear integer programming (NLIP) model, which jointly optimize bitrate adaptation and resource allocation for multiple users. This model takes video quality, playback interruptions, and quality oscillations as linear constraints to maximize multicast users' Quality of Experience (QoE). Due to NP-Hardness of this problem, we propose a heuristic greedy algorithm, which can work out the optimal or near-optimal solution with low time complexity. The evaluation results demonstrate that our method can achieve Pareto Optimality of the system utility, and maximize users' QoE while ensuring fairness.

With the development of Virtual Reality (VR) technology, the growing number of VR users puts tremendous pressure on network bandwidth. The tile-based scheme is proposed to reduce the transmission size of 360° video and improve bandwidth utilization. However, when the Field of View (FoV) of the user changes unexpectedly, the tile-based scheme will cause video distortion and quality switching by unacceptable delay. Therefore, many methods are proposed to cache the tiles that users are most likely to playback in Cloud/Edge to decrease delay. However, the dynamic adaptive bitrate delivery and the caching decision is a complex joint optimization problem, which will be a dimensional explosion problem when the scale of users and videos is large. In this paper, we design a QoE-based 360° video adaptive bitrate delivery and caching scheme aiming to maximize the quality of experience (QoE) of multi-user and ensure the fairness of users. To solve this optimization problem which is proved to be NP-Hard, we propose a bitrate selection and caching decision algorithm by greedy strategy. Numerical simulation results demonstrate that our algorithm significantly improves cache hit rate and QoE performance compared with other algorithms with fairness guaranteed.

Caching content in routers is the most significant feature of Named Data Networking (NDN) and therefore the cache performance is increasingly being concerned for NDN deployment. The default cache policy of NDN is Leave Copy Everywhere (LCE) that leaves the copies in each node the data packet passed, and most of the copies will not be requested again, which leads to the waste of cache resources. The Betweenness Strategy caches data in the node with the maximal betweenness value, which causes a high replacement rate. Considering the betweenness of nodes and the popularity of content, as well as the filter effect of cache, we propose Betweenness and Edge Popularity strategy (BEP) which caches the most popular content in the most important nodes. We also conduct comprehensive simulations based on ndnSIM. By evaluating BEP and other cache replication strategies on a virtual topology, it is indicated that BEP can achieve higher performance in terms of the cache hit ratio, server load and average delay.

Explosive growth in video traffic volumes incurs a high percentage of redundancy in today's Internet, following the 80–20 rule. Fortunately, the advanced in-network cache is considered as an effective scheme for eliminating the repetitive traffic by caching the popular content in network nodes. Besides, the emerging software-defined networking (SDN) enables centralized control and management, as well as the collaboration between network devices and upper applications. Moreover, the Network Functions Virtualization is also developed to support for customized network functions, including caching and streaming. This inspires us to design an SDN-assisted multimedia streaming Video-on-Demand system, integrating in-network cache, to improve the quality of service. The designed architecture is capable of reducing the redundant traffic via the reusable duplications. In particular, it can achieve greater performance gains by deploying specific scheduling policy. We further propose a variable-length interval cache strategy for RTP streaming, which can realize the self-adaptive adjustment of the size of cached video segments based on their access patterns. Our goal is to efficiently utilize the limited storage resources and increase the cache hit ratio. We present the theoretical analysis to demonstrate the attainable performance of the proposed algorithm; furthermore, the integrated system design is implemented as a prototype to show its feasibility and applicability. Ultimately, emulation experiments are conducted to evaluate the achievable performance improvement more comprehensively.

A receiver-driven adaptive layer switching algorithm is proposed for adapting the video bitrate to match the achievable network throughput. It relies on a QoS-constrained equivalent bandwidth estimator employed at the receiver, which is used for triggering the adjustment of video layers at the video source. Simulations are conducted to illustrate its efficiency by showing that it is capable of accommodating different channel qualities without their prior knowledge.