Augmented Reality (AR) and Multimodal Large Language Models (LLMs) are rapidly evolving, providing unprecedented capabilities for human-computer interaction. However, their integration introduces a new attack surface for Social Engineering (SE). In this paper, we systematically investigate the feasibility of orchestrating AR-driven Social Engineering attacks using Multimodal LLM for the first time, via our proposed SEAR framework, which operates through three key phases: (1) AR-based social context synthesis, which fuses Multimodal inputs (visual, auditory and environmental cues); (2) role-based Multimodal RAG (Retrieval-Augmented Generation), which dynamically retrieves and integrates social context; and (3) ReInteract social engineering agents, which execute adaptive multiphase attack strategies through inference interaction loops. To verify SEAR, we conducted an IRB-approved study with 60 participants and build a novel dataset of 180 annotated conversations in different social scenarios (e.g., coffee shops, networking events). Our results show that SEAR is highly effective at eliciting high-risk behaviors (e.g., 93.3% of participants susceptible to email phishing). The framework was particularly effective in building trust, with 85% of targets willing to accept an attacker's call after an interaction. Also, we identified notable limitations such as authenticity gaps. This work provides proof-of-concept for AR-LLM driven social engineering attacks and insights for developing defenses against next-generation AR/LLM-based SE threats.

Invariant user preference learning is a core task in recommender systems. Accurately modeling user preferences is crucial, as it directly impacts the quality of recommendations. However, heterogeneous user preferences in feedback data often exhibit mixture distributions, which obscure invariant preferences and introduce bias. Existing methods typically address this issue by partitioning feedback data into multiple environments and learning invariant preferences across them. Nonetheless, these approaches often lack theoretical guarantees for environment construction and fail to capture the dynamic nature of user preferences across different environments. Along these lines, we propose a novel framework, Invariant and Environment-specific Preferences for unbiased recommendation (IEPref). IEPref leverages auxiliary information as a reliable signal to guide the environment classifier in partitioning the environment, thereby enabling the learning of more stable and generalizable user preference with theoretical guarantees. Additionally, we design environment-specific proxy modules to capture context-dependent preference patterns unique to each environment. The environment classifier assigns each user-item interaction to its corresponding latent environment, and both invariant and environment-specific preferences are integrated for recommendations. Extensive experiments on five real-world datasets demonstrate that IEPref achieves superior performance over existing baselines, effectively mitigating recommendation bias while preserving personalized modeling capabilities.

Satellite communication environments, characterized by dynamic links, exacerbate packet reordering, which leads to fundamental limitations in QUIC’s loss detection mechanism. A fixed packet threshold may misinterpret reordered packets as lost, leading to spurious retransmissions and throughput degradation. To address the challenge, we propose GRU-QUIC, a machine learning-enhanced QUIC protocol with packet reordering resilience for satellite networks. First, we model the relationship between the probability of spurious packet loss due to reordering and the throughput of QUIC, providing a theoretical basis for performance optimization. Second, we design a gated recurrent unit (GRU)-based prediction module on the receiver side to predict the next slot packet reordering displacement by learning the temporal correlation of packet reordering sequences. The sender then uses the reordering displacement indicated by the GRU to dynamically adjust the packet threshold of loss detection, effectively alleviating the problem of packet loss misjudgment due to packet reordering. Experiments demonstrate that GRU-QUIC significantly enhances the performance of QUIC transmission while maintaining protocol compatibility. In a simulated satellite network, GRU-QUIC effectively reduces unnecessary retransmissions and shortens file download completion time compared to standard QUIC.

In this paper, we propose the IFresher framework to improve the timeliness of multi-agent mobile augmented reality (MAR) systems. Existing works have made strides in accuracy-latency trade-offs, but fail to directly address realtime task responsiveness and multi-agent contention challenges. To bridge this gap, we introduce the concept of the age of analytics information (AoAI), which quantifies the combined impact of video analytics (VA) accuracy, transmission delay, and computational efficiency. By deriving a closed-form expression for AoAI, IFresher establishes a central control mechanism that jointly optimizes bandwidth allocation and video configuration to minimize AoAI while ensuring accuracy. Due to the mixed-integer nonlinear characteristics of the problem and the fact that each agent only has local observations, the problem is reformulated into a decentralized partially observable Markov decision process (Dec-POMDP). We propose a multi-agent reinforcement learning (MARL) algorithm, named convex-embedded transformer QMIX (CTQMIX), using the centralized training and decentralized execution (CTDE) framework for agent collaboration. Specifically, the convex optimization ensures optimal bandwidth distribution, and the transformer captures temporal dependencies between observations and actions across time steps to improve decisionmaking in dynamic environments. Evaluations with real-world experiments show that the CTQMIX outperforms state-of-theart (SOTA) algorithms.

This paper presents an edge-assisted massive mobile live streaming (MMLS) framework named LOCA, integrating chunk-level analysis and long-term optimization to design resource allocation, bitrate adaptation, and source selection strategies. The proposed method ensures sustained real-time video delivery while minimizing latency and communication costs. Firstly, a chunk-level analysis of the entire process of video streaming is introduced, aiming at modeling fetch queue waiting time and rebuffering duration in each time slot. By embedding this mathamatical model into consideration, a long-term optimization is formulated to minimize rebuffering and communication overhead while maintaining high video qualities for massive users. Leveraging Lyapunov optimization, we transform this problem into a computationally tractable form. Further simplification via linearization achieves near-optimal solutions by adopting the mixed-integer linear programming method with enhanced computational efficiency. Simulation results demonstrate superior stability and long-term performance compared to the state-of-theart and baseline methods, validating the framework's efficacy in MMLS scenarios

Due to the intensive computing resource requirements, real-time video analytics applications typically need to transmit video to a server. However, the transmission inevitably suffers from network bandwidth limitations and fluctuations, making it challenging to guarantee video analytics performance. In this paper, we aim to maximize video analytics accuracy while maintaining low latency and frame loss rate, and propose a joint segment and frame bitrate adaptation (JSFBA) framework for real-time video analytics, which incorporates two reinforcement learning-based algorithms to adapt to bandwidth at both the segment and frame levels. Initially, considering the effect of video encoding on video analytics, we employ a bitrate control method to design a segment-level bitrate adaptation (SLBA) algorithm with a unique reward function. Based on the historical information of the video segments, SLBA selects the appropriate bitrate for each segment. Subsequently, by leveraging the ability to generate multiple bitrates in scalable video coding (SVC), we design a frame-level bitrate adaptation (FLBA) algorithm, which adapts to bandwidth in a more fine-grained manner by determining the number of layers sent for each frame. Extensive experiments on large-scale network traces reveal that JSFBA effectively balances various video analytics performance metrics and achieves maximum utility compared to state-of-the-art solutions.

Satellite networks are crucial for future 6G communications, offering global coverage. However, traditional Transmission Control Protocol (TCP) struggles with poor stability in dynamic satellite networks. In contrast, QUIC is capable of providing stability in such environments thanks to features like seamless connection migration. Nevertheless, the loss detection and recovery strategy of QUIC needs improvement in satellite networks with relatively high transmission errors. To address this, we propose Polar-QUIC, an adaptive packet-level polar coding-assisted QUIC scheme. By leveraging the robust error correction capabilities of polar codes, we design a packet-level encoder/decoder, loss detection tuning module, and adaptive redundancy module that combines packet loss rate and congestion window size. Experimental results demonstrate that Polar- QUIC effectively reduces the average Download Completion Time (DCT) of QUIC in satellite networks. Specifically, Polar-QUIC minimizes the average DCT by up to 35.8% and 74.5%, respectively, compared to general forward error correction schemes such as Reed-Solomon Codes and Random Linear Codes.

Due to the huge file size of virtual reality (VR) videos, providing high-quality and low-latency live VR videos to multiple users using existing network architectures becomes exceptionally challenging. Although some tile-based multicast schemes can reduce the amount of transmitted data, users with differentiated channel conditions and interaction behaviors still need to be effectively grouped. In this paper, we first model VR video multicast scheduling in the case of orthogonal multiple access (OMA), where the objective of maximizing the sum of user experience qualities forms an NP-hard integer nonlinear programming problem. By analyzing the time complexity source of the exhaustion-based global optimal algorithm and the impact of two factors on grouping, we propose a scheduling algorithm with very low complexity based on dynamic programming in the OMA case and a field-of-view ratio sorting and grouping method for non-orthogonal multiple access (NOMA) scenarios. In extensive actual viewport experiments, the proposed algorithm outperforms the state-of-the-art multicast scheme adopting OMA by 17.86% with negligible time complexity, making it suitable for deployment in realistic environments. The proposed sorting method increases the system utility of NOMA’s optimal solution by 13.13%.

In this paper, we propose an edge selective sharing architecture (ESSA) for massive mobile video streaming (MMVS) and develop cross-layer optimizations for joint user scheduling and power allocation (JUSPA) in ESSA. This work aims to ensure users' high quality of experience (QoE) by efficiently using network resources over mobile edge computing (MEC)-assisted massive multiple-input and multiple-output (MIMO) networks. Initially, by taking advantage of the analytical and empirical characteristics of video streaming, ESSA is able to maintain MMVS by activating only a portion of facilities in wireless access networks (WANs) without sacrificing video quality. Following this, a cross-layer optimization problem is formulated for JUSPA. We simplify the problem into a generalized assignment (GA) problem by discussing the peculiarities of MMSV, whose approximate solution can be obtained in polynomial time. Moreover, load balancing based on user density is integrated to reformulate a capacitated facility location (CFL) problem solvable with low overall time complexity, effectively mitigating the increased overheads of MEC units and enhancing the overall performance of MMVS. Numerical results indicate that our ESSA with JUSPA schemes for MMVS achieves better streaming fluency and transmission efficiency than alternative methods.

QUIC has drawn extensive attention in supporting low latency and secure Internet of Things (IoT) communications due to its efficient handshake and default end-to-end encryption. However, in Wi-Fi-enabled IoT communications with contentions for shared media, QUIC’s inherent acknowledgment (ACK) policy may induce non-negligible control overhead and limited data throughput. To address the problem, this article designs and implements an ACK frequency optimization scheme for QUIC by exploiting the tailored bandwidth-delay product (BDP) at the receiver, named QUIC-BDP. To accurately estimate real-time BDP, we design an “ACK-PING” strategy to compensate for the accuracy of round-trip timing estimation and utilize exponential averaging and sliding window filtering for stable bandwidth estimation. Experimental‘ results show that our proposed QUIC-BDP balances between the robustness and throughput performance while maintaining stable performance in lossy cases, with a reduced energy cost. Particularly, QUIC-BDP achieves up to a 67% gain in goodput compared to the original QUIC, and it improves goodput by up to 38% and 28% compared to existing solutions MSQUIC and QUIC-1:10, respectively. In addition, QUIC-BDP reduces energy cost by up to 50% compared to the original QUIC.

Recently, innovative technologies such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Multi-Sensorial Media (mulsemedia) have introduced new sensorial effects including vibration, smell, airflow, etc. to human life. These effects which have been largely deployed for entertainment, and gaming have positively impacted user satisfaction. This paper explores the potential of mulsemedia in the education context. It describes a novel Dynamic Adaptive Streaming over HTTP (DASH)-based Multi-sensory Media Delivery Solution (DASHMS) which supports adaptive mulsemedia content distribution based on the operational environment which includes network, device, and user settings.DASHMS was evaluated in a real-life educational experiment involving 44 students in an Irish university. The evaluation focused on both learner satisfaction, and the impact on learning. The results demonstrate the potential of adaptive multi-sensorial media delivery to result in a statistically significant increase in user experience. In terms of benefit to learning outcomes however, it was only memory recall which was statistically improved in the experiment.

5G technology is expected to enable a plethora of new applications with distinct requirements. Provisioning resources to accommodate such applications implies having a flexible network infrastructure that can be tailored to the specific needs of each application. This can be achieved through network slicing. Still, several applications might request network slices, but their request may not be fulfilled due to lack of resources or lack of coverage and provisioning such resources is a cumbersome task. This paper describes an architecture that facilitates the dynamic leasing of resources among network operators to support cross-domain services. The cornerstone of this architecture is a brokering layer, called DBB, that relies on a blockchain-based bidding system to request resources and evaluate resource provisioning offers. The paper also presents a simulation-based use case scenario that illustrates the need for DBB and which was used to evaluate the performance of the proposed architecture.

The latest increase in multi-view video solutions, including those for telepresence, commercial conferencing, remote collaboration, etc. requires support for the high-quality delivery of large amounts of content data. Meanwhile, the extensive proliferation of wireless network access technology and multiple network interfaces on modern devices prompt the network transmission performance over various access networks. Diverse multipath-based multi-view streaming and adaptive delivery solutions were proposed, but they do not enable differentiation between streams. This paper proposes MVP-DASH, a priority-aware adaptive multi-view video streaming scheme based on the MPEG-DASH framework. MVP-DASH enables improved visual quality for high-priority streams, while maintaining acceptable quality levels for low-priority streams, primarily when delivered over a dynamic network environment. The experimental evaluation of the MVP-DASH demonstrates the effectiveness of the proposal in terms of achieving higher video quality and fewer video quality switches in comparison with alternative approaches.

Today, the dynamic network environment poses severe challenging issues to multimedia streaming services that account for an enormous part of network traffic all over the world. Dynamic adaptive streaming over HTTP (DASH) facilitates seamless video playback by allowing for dynamic adjustment of the video bitrate to the ongoing network situation. Despite several attempts, there is still a challenge to design solutions which use DASH to adjust video delivery to the dynamic network environment and achieve high user quality of experience levels. This paper presents a novel DASH-based throughput and buffer occupancy-based adaptation (TBOA) algorithm to provide an improved streaming experience for remote users. TBOA was compared against alternative solutions such as FDASH and SFTM in single- and multiple-client scenarios. Testing results show how TBOA selects higher video bitrates while performing fewer video bitrate switches and reduces the risk of buffer underrun in comparison with the competitors.

Multimedia synchronization involves a temporal relationship between audio and visual media components. The presentation of “in-sync” data streams is essential to achieve a natural impression, as “out-of-sync” effects are often associated with user quality of experience (QoE) decrease . Recently , multi-sensory media (mulsemedia) has been demonstrated to provide a highly immersive experience for its users. Unlike traditional multimedia, mulsemedia consists of other media types (i.e., haptic, olfaction, taste, etc.) in addition to audio and visual content. Therefore, the goal of achieving high quality mulsemedia transmission is to present no or little synchronization errors between the multiple media components. In order to achieve this ideal synchronization, there is a need for comprehensive knowledge of the synchronization requirements at the user interface. This paper presents the results of a subjective study carried out to explore the temporal boundaries within which haptic and air-flow media objects can be successfully synchronized with video media. Results show that skews between sensorial media and multimedia might still give the effect that the mulsemedia sequence is “in-sync” and provide certain constraints under which synchronization errors might be tolerated. The outcomes of the paper are used to provide recommendations for mulsemedia service providers in order for their services to be associated with acceptable user experience levels, e.g. haptic media could be presented with a delay of up to 1 s behind video content, while air-flow media could be released either 5 s ahead of or 3 s behind video content.

In the current telecommunication environment, network operators are trying to cope with a significant increase in data traffic by adopting different solutions to expand their network capacity. One of these solutions is the convergence of next generation wireless networks (e.g., HSDPA, LTE and WiMAX) which involve closely interworking of existing 2G/2.5G/3G networks with the new next generation networks in terms of handover and network selection. However, the diversification in mobile devices and the heterogeneity of the wireless environment make the seamless always best connectivity of mobile users a challenge for the service providers. We propose RLoad, a novel Reputation-based Load-balancing Network Selection Strategy for heterogeneous wireless environments, built on top of the IEEE 802.21 Media Independent Handover (MIH) standard. The proposed solution makes use of a reputation-based mechanism to select the most appropriate set of networks for the mobile user and a load balancing mechanism to distribute the traffic load among the networks by making use of the Multipath TCP (MPTCP) protocol. Preliminary simulation results show significant benefits when using the proposed RLoad solution.