Live volumetric video streaming enables immersive user experiences but poses significant challenges due to the high bandwidth requirement that 3D representations entail. Recent research has focused on reducing volumetric video bandwidth, but it struggles to effectively address temporal redundancy under real-time constraints, limiting its applicability for live streaming scenarios. To address these challenges, we present DeltaStream, a novel live volumetric video streaming system that efficiently encodes 3D point clouds by leveraging 2D information. By utilizing 2D RGB and depth frames, DeltaStream efficiently infers inter-frame changes to reduce the streaming bandwidth of volumetric video. Furthermore, DeltaStream introduces an adaptive block-based approach that can reduce the client-side decoding load. Through extensive evaluations, our results demonstrate that DeltaStream reduces bandwidth by up to 71% with 1.63× faster decoding speed while maintaining visual quality compared to state-of-the-art systems.

We propose ThermicVib, a wearable multimodal haptic glove that enhances the active perception of thermo-tactile interactions with virtual objects by integrating thermal referrals and vibrotactile phantom sensations. By fusing multimodal sensory illusions through flexible thermoelectric devices (FTED) and linear resonant actuators (LRAs), we aim to support dynamic thermal sensation adaptive to the user’s action in virtual reality (VR). Here, we developed an algorithm to render a whole-hand thermal sensation while accommodating contact and noncontact heat conditions. Based on the computed heat, we propose a simultaneous thermal and tactile rendering approach to enable dynamic thermal sensation. The user study validated the capability of our interface to support various whole-hand thermal sensations.

The Society of Automotive Engineers (SAE) has specified wireless channel congestion control algorithm for vehicle-to-everything (V2X) communication: J2945/1 for Dedicated Short Range Communication (DSRC) and J3161/1 for cellular V2X (C-V2X) environment. Although J2945/1 utilizes both transmit (Tx) power control and message rate control, the more recently specified J3161/1 standard for C-V2X environment has opted to abandon the power control. It is understandable as previous studies have demonstrated the inefficacy of power control in J2945/1, resulting in the rate control solely fighting congestion. Consequently, channel congestion leads to a fast increase in packet inter-reception time (PIR) and potentially tracking error. This letter advocates for the reinstatement of Tx power control in J3161/1 as a means to minimize PIR, while proposing a distinct approach from J2945/1 to show the feasibility of an effective combined control. Based on vehicle traffic engineering principles, the approach switches control modes to use power control to cope with increased vehicle density and rate control with increased channel utilization. It leads to significant improvements in packet delivery characteristics while more effectively managing channel load.

Co-existence between autonomous vehicles (AVs) and human-driven vehicles expected in the next few decades poses a problem for AVs to infer human drivers’ intents nearby and cope with them safely and efficiently. To address this issue, we develop a light-weight deep learning model for a connected autonomous vehicle (CAV) to infer intents in a safety-critical case of lane changes made by human-driven vehicles. Through experiments with the real trajectory dataset NGSIM, we show that a simple Multi-Layer Perceptron (MLP) model can predict lane change events with high accuracy comparable with more sophisticated models. The model is intentionally designed to work with the simplest 3-vehicle topology to foster real-time execution on the resource-constrained computing platforms on AVs. Still, the model achieves 85% accuracy over 5 to 8 seconds prediction horizons so that AVs can have enough time to prepare for an upcoming lane change event.

Non-terrestrial network (NTN) is a technology that enables mobile devices such as smartphones to directly communicate with Low Earth Orbit (LEO) satellites and access Internet services from anywhere on the globe. Although initial commercial releases offer only limited bandwidth, the technology is expected to rapidly expand its capacity with "mega-constellations." In this paper, we explore novel configuration options of the NTN architecture that will help boost the TCP throughput performance of an NTN user. We first demonstrate that traditional performance-enhancing proxies (PEPs) deployed on only terrestrial gateways do not have an overwhelming merit over end-to-end TCP Hybla. However, we show that an on-board PEP strategically deployed on satellites can achieve superior performance than both TCP Hybla and the PEP on the terrestrial gateway.

None of the current V2X congestion control standards on the wireless medium access layers have provisions for the speed difference of vehicles. The situations where vehicles have wildly different speeds are more common than not. Even if a more recent ETSI standard specifies facilities layer congestion control that considers the vehicle speed, the access layer congestion control that performs gate-keeping is still speed-agnostic and can cause safety problems. Therefore, the access layer congestion control standards should also be generalized to cater to the safety communication requirement associated with vehicle speed. In this paper, we propose a new access layer congestion control for V2X communication that accounts for the speed difference between vehicles. We demonstrate such a scheme can effectively differentiate the congestion control based on speed differences, facilitating driving safety especially for high-speed vehicles passing by low-speed vehicles.

The channel width for Intelligent Transportation System (ITS) band for vehicle-to-everything (V2X) system is expected to be relatively narrow. It implies that effective congestion control mechanisms will be essential. A well-established standard mechanism is the Cooperative Awareness Message (CAM) generation rule that drops unnecessary periodic beacons. Dropping packets from a periodic packet stream produces a non-periodic traffic. It renders the distributed Semi-Persistent Scheduling (SPS) algorithm exposed to resource waste and packet collision problems. The problems can be tackled by supplementing SPS with machine learning (ML) that predicts the transmission times of non-dropped CAM packets and reserves a transmit resource at the predicted time. The proposed approach is shown to significantly extend the distance up to which the target packet reception ratio (PRR) is met, compared to the current SPS algorithm.