The increasing demand for mobile 360-degree video streaming drives the need for highly immersive and smooth user experiences. However, maintaining a smooth Quality of Experience (QoE) for 360-degree video is challenging due to strict network requirements, particularly on variable mobile networks. These challenges can lead to poor streaming quality and cyber/motion sickness within immersive settings. Addressing these issues requires understanding the impact of network conditions and scalable testing under realistic scenarios. For this purpose, this paper presents an empirical analysis correlating real-world 5G radio Quality of Service (QoS) metrics from three U.S. operators with YouTube 360-degree video QoE. Our findings include a valuable dataset linking 5G radio QoS to video QoE.

Holographic-type communication demands multi-Gbps throughput and ultra-low latency, posing significant challenges to current 5G networks. This demo work presents a programmable testbed built on the P7 network emulator, which overcomes the throughput constraints of conventional platforms. Our demonstration deploys three edge computing slices—one at a far edge and two at near edges—to support volumetric media streaming directly to VR head-mounted display. By facilitating precise adjustment of network metrics such as latency and packet loss across the P7-enabled slices, the testbed allows users to visually observe the resulting effects on holographic content in VR. Although automated measurement of Quality of Experience (QoE) parameters are not incorporated, the testbed provides a valuable experimental environment where researchers can subjectively evaluate the relationship between network conditions and perceived quality, thereby gaining insights into optimizing network performance for enhanced Quality of Service (QoS).

Evaluating the Quality of Experience (QoE) for over-the-top (OTT) video streaming is crucial due to increasing video traffic demands for delivering satisfactory user experiences. This work presents and evaluates a rich dataset capturing real-world 5G network performance and its impact on YouTube video streaming QoE. We utilized two US-based 5G networks to stream 2D YouTube videos and monitor cellular Key Performance Indicators (KPIs) at 1-second granularity. These KPIs include Channel Level Metrics (CLMs), such as RSRP, RSRQ, and SNR, and objective QoE metrics for various scenarios, including indoor and outdoor environments with different mobility conditions. We correlate CLMs KPIs in 5G Standalone (SA) and Non-Standalone (NSA) deployments with YouTube's objective QoE. Our study shows performance differences where 5G SA encounters more streaming stalls during outdoor mobility than NSA, while SA supports higher resolutions in static indoor settings.

This paper presents the design and development of an indigenous modular 5-degree-of-freedom (5-DOF) robotic manipulator aimed at diverse applications. The arm is built from locally sourced materials and cost-effective components to ensure affordability without compromising functionality. This research work describes materials selection, mechanical CAD modeling (SolidWorks), and the joint/actuator configuration. The control system uses open-source microcontrollers (e.g., ESP32, Arduino) and is integrated with sensors. Comprehensive simulations (static stress, dynamic motion, thermal and vibration analyses) were performed to validate the design. A prototype was fabricated and tested, demonstrating the expected reach, payload capacity, and positioning precision. Potential applications in industry, aerospace/space missions, healthcare, defense, and education are discussed. The results indicate that the proposed design meets the objectives of affordability and versatility for multidisciplinary use. Its modular architecture and low-cost parts make it suitable for a range of multidisciplinary applications. Overall, the research and innovative work demonstrates the feasibility and benefits of developing locally engineered robotic manipulators for diverse industries.

The growing popularity of eXtended Reality (XR) is being driven by technological advancements and the demand for advanced immersive digital experiences, including the vision around the metaverse. Within the XR realm, 360-degree immersive video streaming is essential for Virtual Reality (VR) adventures and experiences. The use of E2E encryption for content delivery in 360-VR streaming poses challenges for network operators, making it difficult to manage their networks and assess potential Quality of Experience (QoE) impairments, specifically in 5G and beyond networks. Therefore, we propose a Machine Learning (ML) approach for inferring 360-VR video QoE metrics from network-level encrypted traffic. Our solution uses packet-level information for feature engineering, which serves as input for the ML model to predict target QoE estimators. We evaluate our solution using real 4G and 5G drive test traces with encrypted VR traffic using HTTPS and QUIC protocols. The experimental results show that the trained ML model yields reasonable accuracy with minimal residual error in predicting target VR QoE for both HTTPS and QUIC. Network operators can use such a model to passively monitor the real-time QoE of encrypted VR video sessions and optimize network performance.

The exponential increase in Dynamic Adaptive Streaming over HTTP (DASH) based video traffic with endto-end encryption poses many challenges for Mobile Network Operators (MNOs). To improve user-perceived video quality, MNOs must be aware of the end user’s Quality of Experience (QoE) by exploring network level Quality of Service (QoS). On the contrary, the network edge facility provides proximity for performing an intelligent operation to maintain smooth QoE from the network level QoS measurement. Therefore, in this work, we propose EFFECTOR, a framework to showcase lightweight in-band QoS features measurement technique at edge nodes from encrypted DASH video traffic. EFFECTOR uses an emulated environment with real 4G and 5G drive test traces to generate video traffic. Moreover, this work provides a massive dataset for analyzing the impact of 4G and 5G technology on video quality in the form of Interactive Jupyter Notebooks. The proposed framework is ideal for investigating QoS extracted from the network’s edge and finding its relations with QoE to ensure better video quality for end-users.

The outbreak of the novel coronavirus SARS-CoV2 has dramatically changed the world and has been a severe health threat in 2020 and 2021. In this article, an agent-based simulation model of pedestrian dynamics is proposed for classroom-type indoor spaces (e.g., classroom, auditorium, food court, and meeting room), which will help organizations such as universities to evaluate alternative policies (namely entrance and exit policy, seating policy, and room layout) concerning the contact-caused risk associated with activities in such places during a pandemic situation. In particular, the proposed work focuses on solving the indoor seat allocation and traffic movement problem while practicing appropriate physical distancing measures. The proposed seating policy evaluates the distance of a seat from the doors and pathways facilitating the evaluation of contact-caused risk associated with the pathway and indoor area movement. Various statistics from two perspectives, risk, and logistics, are reported in the simulation results. The risk metrics used in evaluating different policies include average exposure duration and an average number of contacts with others. To develop a highly realistic crowd simulation considering physical distancing and human intervention nature, deadlock detection and resolution mechanisms are incorporated. From this study, it has been observed that the proposed social distancing (SD) seating policy and zonal exit policy can significantly reduce the contact number and exposure duration at a higher occupancy level. The proposed work helps the organizational policymakers to evaluate different policies and ensure the safe operation of the organizations under pandemic situations.Note to Practitioners—This article was motivated to ensure safer and efficient operations within indoor facilities by evaluating contact-caused risks and entrance/exit policies during a pandemic situation like coronavirus disease (COVID-19). Most of the recent works related to COVID-19 disease propagation focus on the evaluation of disease transmission at an organization level, however, limited studies focus on the operational aspect of indoor facilities within the organization. To bridge this gap, this article utilizes an agent-based modeling approach to model and understand the pedestrian dynamics in the classroom-like facilities considering physical distancing, seat assignment, and entry/exit policies. Comprehensive simulation modeling and analysis allows the amalgamation of real data, including layout, class schedules, seating arrangement, and allowable capacity to perform various what-if analyses under different policies. This article proposes and evaluates seating assignment based on the associated number and duration of contacts during movements in the pathway and entrance/ exit area of a confined space. In addition, this work evaluates different indoor layouts (e.g., classroom, meeting room, and office) and exit policies (e.g., zonal and non-zonal) at different occupancy levels for appropriate decision support. The proposed approach will aid organizations (e.g., educational institutions, corporate offices, and recreational facilities) to evaluate necessary policies (namely entrance and exit policy, seating policy, and room layout) for safe indoor operations with respect to the minimum contact-caused risk associated with the activities. As a future direction, the outputs from the proposed work can be utilized to obtain the realistic input parameters needed for organization-wide disease propagation models, which will provide decision support at an organizational level.

Fifth Generation (5G) networks provide high throughput and low delay, contributing to enhanced Quality of Experience (QoE) expectations. The exponential growth of multimedia traffic pose dichotomic challenges to simultaneously satisfy network operators, service providers, and end-user expectations. Building QoE-aware networks that provide run-time mechanisms to satisfy end-users' expectations while the end-to-end network Quality of Service (QoS) varies is challenging, and motivates many ongoing research efforts. The contribution of this work is twofold. Firstly, we present a reproducible data-driven framework with a series of pre-installed Dynamic Adaptive Streaming over HTTP (DASH) tools to analyse state-of-art Adaptive Bitrate Streaming (ABS) algorithms by varying key QoS parameters in static and mobility scenarios. Secondly, we introduce an interactive Jupyter notebook and Binder service providing a live analytical environment, which processes the output dataset of the framework and compares the relationship of five QoE models, three QoS parameters (RTT, throughput, packets), and seven different video KPIs.

Intent-Based Networking (IBN) proposals are based on autonomous closed-loop orchestration architectures that monitor and tune network performance. To this end, IBN defines high-level policies and actions implemented by a closed-loop system. This work demonstrates a Closed Control Loop (CCL) architecture for video service assurance using Machine Learning (ML) based Quality of Experience (QoE) estimation at edge nodes. As part of the solution, network-level Quality of Service (QoS) metrics patterns (e.g., RTT, Throughput) collected through flow-level monitoring are used to build a QoS-to-QoE correlation model tailored to specific target network regions, user groups, and services, in our case DASH video streaming. The demo will showcase the CCL workflow triggering the Orchestrator to take appropriate network-level actions to overcome network QoS degradations and restore the QoE target based on the intent associated with the video service.