A dynamic 3D model is a key component in a Virtual Reality environment. To reduce the processing requirement while preserving the user experience, an adaptive Level of Detail (LoD) of a 3D model based on the viewing distance has been proposed. In this paper, aiming to optimize the generation and selection of LoD versions, we investigate the effects of Level of Detail and viewing distance on user perception of dynamic 3D models in a VR environment. We conducted a subjective experiment with five representative dynamic 3D models under four Level of Detail and five viewing distance settings in a Virtual Reality environment. Experimental results show that the MOS score increases by 0.3 to 2.1 as the viewing distance increases from$d=4 \mathrm{m}$to$d=20 \mathrm{m}$. Moreover, removing up to 50% of a dynamic 3D model's faces has a negligible impact on the user's perception. An evaluation of popular objective quality metrics reveals that video PSNR has the highest correlation with subjective scores.

3D meshes are one of the main components of Virtual Reality applications. However, a huge amount of network and computational resources are required to process 3D meshes in real time. A potential solution to this challenge is to dynamically adapt the Level of Detail (LoD) of a 3D mesh based on the object’s position and the user’s viewpoint. In this paper, we conduct a subjective study to investigate users’ quality perception of 3D meshes with dynamic Levels of Detail in a Virtual Reality environment. The subjective experiment is carried out with five 3D meshes of different characteristics, four Levels of Detail, and four distance settings. The results of the experiment show that the impact of the dynamic level of detail depends on both the position of the 3D object in the virtual world and the number of vertices of the original mesh. In addition, we present a quality model that can accurately predict the MOS score of a LoD version of a 3D mesh from the number of vertices and the distance from the viewpoint.

Point cloud video is an effective method to represent moving objects for metaverse applications. Real-time streaming of point cloud video can offer truly immersive experiments for metaverse users. However, point cloud videos have an extremely high data rate and demand significant processing resources for compression and rendering at the user device. To address the above challenges, this paper presents a novel server-driven view-aware point cloud video streaming system that can effectively reduce bandwidth requirements by combining hidden point removal and video-based point cloud encoding. Experiment results show that the proposed method can reduce the bandwidth requirement by up to 24% compared to the baseline method.

Point cloud video streaming over networks is challenging because of the high data rate of uncompressed point cloud data. Adaptive point cloud video streaming has been proposed to deal with this challenge. However, temporal quality variation and stalling might occur under unstable network conditions, potentially degrading users’ Quality of Experience (QoE). This paper aims to evaluate and model the impacts of temporal quality variations and stalling on users’ QoE in adaptive point cloud video streaming. We first conduct a large-scale subjective study to construct a QoE database. Then, based on the constructed database, the effects of individual factors are analyzed, and two novel QoE prediction models are presented. Experiment results show that the proposed QoE models achieve high prediction performance in PLCC, SROCC, and RMSE across various point cloud videos.

Realistic Virtual Reality is supported by 360° video, which provides viewers with an immersive watching experience. However, 360°video is bulky in size, while the transmission system has limited ability to provide bandwidth. As such, intelligent adaptive delivery solutions play a crucial role in enabling users to stream high-quality 360°video. In this research, we propose a novel approach for 360-degree video streaming over HTTP/2 that can provide consumers with a good watching experience (QoE) even in varying network circumstances and head-eye movements over time. The proposed method deploys the so-called BBAG algorithm (Buffer and Bandwidth Allocation Algorithm) using Scalable Video Coding to choose appropriate tile layers to resolve the trade-off between network and user adaptivity. With the support of HTTP/2’s stream termination capability, the delivery of late tile layers is terminated to handle abrupt interruptions. By employing multiple buffer thresholds, BBAG is able to adapt bitrates to changes in users’ perspective while watching a 360-degree video. BBAG is proven to improve QoE up to about 90% by maintaining high and stable buffer level, while enhancing average viewport bitrates by about 80% compared to state-of-the-art methods in different scenarios of network bandwidths .

Point cloud is the emerging format for representing real-world objects in VR/AR applications. However, real-time streaming of dynamic point clouds presents challenges due to high data rates and low latency requirements. This paper introduces a novel and bandwidth-efficient streaming approach for scenes consisting of multiple dynamic point clouds over networks with limited bandwidth. The proposed approach dynamically adjusts the Level of Detail (LoD) of individual point clouds based on network conditions and user preferences to optimize the user’s Quality of Experience (QoE). The LoD version selection problem is formulated as a QoE optimization problem, and two real-time solutions are presented for deciding the LoD version for each point cloud. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of visual quality while achieving remarkably low processing time, about 0.01 ms. These findings have the potential to advance seamless user experience.

With the ability to provide an “immersive experience”, 360-degree video-based applications are becoming more and more popular nowadays. In this paper, we propose LL-VAS, a novel adaptation method for low-latency 360-degree video streaming over mobile networks. By applying tile-based streaming, the proposed method allows 360-degree video streaming over resource-constrained mobile networks. In addition, by actively monitoring network throughput at the tile level, the proposed method can detect reductions in network throughput, and adapt video content in a timely manner to avoid re-buffering. Trace-driven experiments show that the proposed method can significantly decrease the number of re-buffering and re-buffering time under strong network throughput fluctuations and small buffer size when compared to reference methods.

Recent years have witnessed the rising of short-form video platforms such as TikTok. Apart from conventional videos, short-form videos are much shorter and users frequently change the content to watch. Thus, it is crucial to have an effective streaming method for this new type of video. In this paper, we propose a resource-efficient prefetching method for short-form video streaming. Taking into account network throughput conditions and user viewing behaviors, the proposed method dynamically adapts the amount of prefetched video data. Experiment results show that our method can reduce the data waste by 37$\sim$52% compared to other existing methods.

The implementation of Internet-of-Things (IoT) applications faces several challenges in practice, such as compliance with Quality-of-Service requirements, resource constraints, and energy consumption. In this context, the joint edge–cloud paradigm for IoT applications can resolve some of the issues arising in pure cloud computing scenarios, such as those related to latency, energy, or privacy. Therefore, an edge–cloud environment could be promising for resource and energy-efficient IoT applications that implement virtual network functions (VNFs) bound together into service function chains (SFCs). However, a resource and energy-efficient SFC placement requires smart SFC embedding mechanisms in the edge–cloud environment, as several challenges arise, such as IoT service chain modeling and evaluation, the tradeoff between resource allocation, energy efficiency and performance, and the resource dynamics. In this article, we address issues in modeling resource and energy utilization for IoT applications in edge–cloud environments. A smart traffic monitoring IP camera system is deployed as a use case for a realistic modeling of a service chain. The system is implemented in our testbed, which is designed and developed specifically to model and investigate the resource and energy utilization of SFC embedding strategies. A resource and energy-aware SFC strategy in the edge–cloud environment for IoT applications is then proposed. Our algorithm is able to cope with dynamic load and resource situations emerging from dynamic SFC requests. The strategy is evaluated systematically in terms of the acceptance ratio of SFC requests, resource efficiency and utilization, power consumption, and VNF migrations depending on the offered system load. Results show that our strategy outperforms some existing approaches in terms of resource and energy efficiency, thus it overcomes the relevant challenges from practice and meets the demands of IoT applications.

Software-Defined Networking (SDN) leverages the implementation of reliable, flexible and efficient network security mechanisms which make use of novel techniques such as artificial intelligence (AI) and machine learning (ML). In particular, these techniques - together with SDN - are the key enablers for the design of anomaly detection methods which are based on efficient traffic flow monitoring. In this paper, we tackle this problem by proposing an efficient anomaly detection framework, denoted as DeepGuard, which improves the detection performance of cyberattacks in SDN based networks by adopting a fine-grained traffic flow monitoring mechanism. Specifically, the proposed framework utilizes a deep reinforcement learning technique, i.e., Double Deep${Q}$-Network (DDQN), to learn traffic flow matching strategies maximizing the traffic flow granularity while proactively protecting the SDN data plane from being overloaded. Afterwards, by implementing the learned optimal traffic flow matching control policy, the most beneficial traffic information for anomaly detection is acquired at runtime—thereby improving the cyberattack detection performance. The performance of the proposed framework is validated by extensive experiments, and the results show that DeepGuard yields significant performance improvements compared to existing traffic flow matching mechanisms regarding the level of traffic flow granularity. In the case of distributed denial-of-service (DDoS) attacks, DeepGuard achieves a remarkable attack detection performance while effectively preventing forwarding performance degradation in the SDN data plane.

Software Defined Networking (SDN) enables flexible and scalable network control and management. However, it also introduces new vulnerabilities that can be exploited by attackers. In particular, low-rate and slow or stealthy Denial-of-Service (DoS) attacks are recently attracting attention from researchers because of their detection challenges. In this paper, we propose a novel machine learning based defense framework named Q-MIND, to effectively detect and mitigate stealthy DoS attacks in SDN-based networks. We first analyze the adversary model of stealthy DoS attacks, the related vulnerabilities in SDN-based networks and the key characteristics of stealthy DoS attacks. Next, we describe and analyze an anomaly detection system that uses a Reinforcement Learning-based approach based on Q-Learning in order to maximize its detection performance. Finally we outline the complete Q-MIND defense framework that incorporates the optimal policy derived from the Q- Learning agent to efficiently defeat stealthy DoS attacks in SDN-based networks. An extensive comparison of the Q-MIND framework and currently existing methods shows that significant improvements in attack detection and mitigation performance are obtained by Q-MIND.

360-degree video is the main content type of Virtual Reality, providing users with immersive viewing experience. In this paper, we propose a novel adaptation method for 360-degree video streaming over HTTP/2, which can provide high viewing experience to users under time-varying network conditions and time-varying user head movements. The proposed method utilizes Scalable Video Coding to solve the trade-off between network adaptivity and user adaptivity. An optimal tile layer selection algorithm is provided. To cope with sudden throughput drops, the delivery of late layers is terminated using HTTP/2's stream termination feature. Also, a tile layer updating scheme is proposed to deal with viewport estimation errors. Experimental results show that the proposed method can improve the average bitrate of viewport by 16-17% compared to a reference method.

Recently, TCP SYN flood has been the most common and serious type of Distributed Denial of Service attack that causes outages of server resource of Internet Service Providers. In another aspect, Software Defined Networking (SDN) has emerged as a new networking paradigm to increase network agility and programmability. SDN is also a promising architecture to deal with the network security issue where we can flexibly change security rules and control incoming flows. In this article, we design an Openflow/SDN network remedy to combat specifically TCP SYN flood. We show security threats for the SDN architecture and exploit SDN capabilities and features to design a SDN-based SYN Proxy (SSP) paradigm to mitigate such TCP SYN threats. Our SSP is proved to be a network-based solution to protect application servers in terms of decreasing number of Half-Open Connections at an application server and increasing probability of successful establishment for a TCP flow connection under TCP SYN Flood attack. Using SSP to support application servers is shown to outperform the case where the servers adopt only the protection scheme of Microsoft Windows server reference model without utilizing SSP. SSP also shows that it can reduce the time a flow entry occupies the switch resource by 94% in comparison with the Avant-Guard solution. In addition, SSP improves the successful connection rate and average connection retrieval time in comparison with the standard Openflow solution.

This article presents the architecture design and experimental evaluation of a QoE-aware flexible-QoS-control next-generation IPTV network. The architecture extends the IMS service control functionality by providing an efficient application-specific service control approach based on user satisfaction on the connectivity. The validation NGN testbed uses OpenFlow network virtualization between individual components.

This work addresses the high-rate session scheduling problem in fractional lambda switching (FlambdaS) networks. With its global phase synchronization and pipeline forwarding (PF) operation, FlambdaS offers promising network performance and scalability over its competitors, e.g., time division multiplexing (such as SONET/SDH) and wavelength division multiplexing (WDM). Yet, Non-Immediate Forwarding (NIF) brings challenging complexity to session scheduling, where other known scheduling methods (e.g. RWTA) are not applicable. A forwarding graph is used to wholly examine the huge schedule space for an end-to-end high-rate NIF session. An efficient scheduling algorithm, eSSM, is proposed to explore all possibilities on the graph and present the optimized non-blocking schedule. Complexity bounds are then devised analytically and experimentally verified under specific circumstances. A low-complexity heuristic is proposed to avoid the complexity of eSSM in low-load networks.

Efficient scheduling for heterogeneous fractional lambda switching (FlambdaS) networks is required but challenging. A heterogeneous network implies bandwidth mismatch between links of varied bit rates. Moreover, when non-immediate forwarding (NIF) is used in FlambdaS, it increases the scheduling complexity exponentially, while decreasing the blocking probability. Thus, NIF scheduling presents a serious challenge for an algorithm to be used in a large heterogeneous FlambdaS network. In this paper, an efficient scheduling algorithm that is combined with a flexible forwarding scheme is presented. The algorithm provides a full scheduling solution for an end-to-end request in heterogeneous FlambdaS networks. Furthermore, the algorithm has linear complexity in single-channel networks and quadratic complexity in multiple-channel WDM networks.

In order to achieve ultra scalable IP packet switching it is essential to minimize "stopping" of the serial bit streams. In our recent experimental work we demonstrated how this can be achieved with an ultra-scalable switching architecture reaching multi-terabits per second (10-100 Tb/s) in a single chassis. The implemented testbed uses only off-the-shelf optical and electronic components. The scalability of this architecture is the direct outcome of how global time (i.e., UTC - coordinated universal time) and pipeline forwarding are utilized. The paper presents the design of a prototype switch and experimental activity with it.

Future Internet traffic will be dominated by on-demand streaming media flows, such as IPTV, 3D/HD video, gaming, virtual reality, and many more. Consequently, future network architectures will need to implementscalable IP packet switchingcapable of offeringpredictable performancesto such applications. Our recent experimental work demonstrated how an IP network can be implemented without "stopping" the serial bit streams. The deployed switch is very simple, scalable to 10-100 terabits per second in a single chassis, and suitable for all optical implementation. The implemented testbed uses only off-the-shelf optical and electronic components and was completed in 9-month.

Time-driven Switching (TDS) networks with non-immediate forwarding (NIF) provides scheduling flexibility and consequently, reduces the blocking probability (blocking is defined to take place when transmission capacity is available, but without a feasible schedule). However, it has been shown that with NIF scheduling complexity may grow exponentially. Efficiently finding a schedule from an exponential set of potential schedules is the focus of this paper. The work first presents the mathematical formulation of the NIF scheduling problem, under a wide variety of networking requirements, then introduces an efficient (i.e., having at most polynomial complexity) search algorithm that guarantees to find at least one schedule whenever such a schedule exists. The novel algorithm uses 'trellis' representations and the well-known survivor-based searching principle.