Multicast is an effective technology for improving the quality of experience (QoE) for multiple users in 360° video services. However, unlike traditional video, 360° video multicast presents several unique challenges, including efficient user grouping, joint optimization of resource allocation and bitrate decision, and the low transmission delay and high computing demands in the transmission. To address these issues, this paper proposes a novel two-tier grouping 360° video streaming multicast scheme. We first formulate an optimization model that ensures QoE while jointly optimizing multi-user two-tier grouping, resource allocation and bitrate decisions. Subsequently, we introduce a two-tier grouping strategy and a corresponding resource allocation approach for each tier, leveraging the Shapley value for fair resource distribution. We then propose a heuristic algorithm, named Two-Tier Grouping Multicast (TTGM), which can achieve an optimal solution in certain iterations with low time complexity. Simulation results demonstrate the effectiveness of TTGM, showing that it significantly outperforms existing algorithms such as BF, oneG, RTOP, VG and Dragonfly in achieving optimal QoE performance regardless of the setting of scenarios.

With advancements in hardware and communication technologies, the demand for 360-degree video applications has surged. However, the overall spectral efficiency is compromised due to the inability of current multicast schemes to accurately identify 360-degree video users with similar characteristics, resulting in improper user grouping. To address these issues, we propose Hcast360, an adaptive user-correlation-based 360-degree video multicast strategy, in which we designed a user correlation metric based on the throughput difference between multicast and unicast to determine the feasibility of multicast among users. This strategy accurately assigns users to appropriate multicast groups, significantly improving the utilization efficiency of wireless resources. We decompose the 360- degree video multicast problem into three sub-problems: user grouping, resource allocation, and bitrate adaptation. To solve these problems, we propose an adaptive grouping algorithm that assigns users to suitable multicast groups without predefining the number of multicast groups. Finally, we employ a genetic algorithm to determine the bitrate for 360-degree video tiles. Experimental results show that our algorithm can improve users' Quality of Experience (QoE) significantly, achieving noticeable gains without any increase in bandwidth usage.

With the rise of edge devices, video analytics has become a key application in edge computing. Single-camera systems are limited by their Field of View (FoV), making them inadequate for complex environments such as traffic intersections. In this paper, we propose Adaptive Cross-Camera Video Analytics (ACCVA), a novel system for intelligent traffic monitoring. ACCVA introduces a novel camera selection algorithm that adaptively chooses the best camera based on vehicle location and historical data, as well as an adaptive retention algorithm to prevent occlusion and recover lost objects. ACCVA establishes dynamic segmentation of camera regions and cross-camera correlation, managing real-time video inference and result sharing. Implemented on the NVIDIA Jetson Orin NX and evaluated with a real-world traffic surveillance dataset, ACCVA significantly reduces end-to-end latency and enhances accuracy compared to existing state-of-the-art systems. ACCVA excels in complex scenarios by balancing low latency and high accuracy, providing an effective solution for intelligent traffic monitoring.

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.

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.