Wi-Fi sensing—especially human activity recognition (HAR)—is emerging as a device-free means of understanding human–space interactions for smart healthcare and elder care. However, most learning-centric HAR systems focus on improving feature-extraction efficiency or advancing neural-network architectures while overlooking physical information—such as location—which is crucial for accurate activity recognition and interpretation. To close this gap, we introduceDWSen, a Dual-Path Wavelet-Attention Kolmogorov-Arnold Network (KAN) framework for joint activity and indoor location sensing. Specifically, DWSen is a compact multi-task model that combines two complementary encoders with a lightweight decoder. First, an Attention-Masked ResNet-1D (AMRN) suppresses noisy subcarriers and thus highlights discriminative temporal patterns. Meanwhile, a Dual Laplace Wavelet Convolution and bidirectional GRU (Bi-GRU) branch (DLWCB) injects physics-aware, band-pass time–frequency cues and captures long-range temporal dependencies in CSI sequences, thereby enhancing the accuracy and robustness of joint activity and indoor location recognition. Thereafter, the dual features are fused at a late stage and decoded by Wavelet-based Kolmogorov-Arnold Network (WavKAN) heads, which incorporate analytical wavelet kernels as priors and combine them with a nonlinear path and batch normalization to generate activity and location logits. Moreover, training employs an uncertainty-based Adaptive Weighted Loss (AWL) that dynamically balances the objectives, thus mitigating negative transfer without manual tuning. Additionally, we collected an activity- and location-sensing dataset, ElderAL-CSI, covering six activities at nine locations performed by three participants. Extensive experiments on ARIL, CSIDA, and ElderAL-CSI demonstrate that our method attains 94.24%/98.20%, 94.37%/99.65%, and 99.56%/85.20% accuracy (activity/location), respectively, and offers competitive or superior performance compared with recent baselines across most settings. These results point to a low-cost, privacy-friendly path toward reliable ambient monitoring in real homes.

Low-latency cloud rendering enables mobile users to experience high-quality, real-time 3D graphics but achieving low Motion-to-Photon (MTP) latency while maintaining smooth playback is a significant challenge. Our real-world measurement study identifies Receive-to-Composition (R2C) latency, caused by ineffective jitter buffer management, as the primary factor contributing to increased MTP latency. To address this, we introduce JitBright, a client-side optimization strategy that dynamically reduces MTP latency through adaptive jitter buffer management. By adjusting buffer levels based on smoothing playback probability and implementing proactive keyframe requests to mitigate frame dependency, JitBright minimizes both active and passive waiting times. Our large-scale evaluation, conducted over 591,000 sessions across diverse network conditions (WiFi, 4G, 5G) and device types, demonstrates significant improvements in user experience. JitBright reduces median R2C latency by up to 87.5%, increases the proportion of sessions meeting strict MTP latency requirements by 6%–27%, and decreases the video freeze rate from 2.4%–2.8% to 0.4%–1.0%.

Addressing the communication and sensing demands of sixth-generation (6G) mobile communication system, integrated sensing and communication (ISAC) has garnered traction in academia and industry. With the sensing limitation of single base station (BS), multi-BS cooperative sensing is regarded as a promising solution. The coexistence and overlapped coverage of macro BS (MBS) and micro BS (MiBS) are common in the development of 6G, making the cooperative sensing between MBS and MiBS feasible. Since MBS and MiBS work in low and high frequency bands, respectively, the challenges of MBS and MiBS cooperative sensing lie in the fusion method of the sensing information in high and low-frequency bands. To this end, this paper introduces a symbol-level fusion method and a grid-based three-dimensional discrete Fourier transform (3D-GDFT) algorithm to achieve precise localization of multiple targets with limited resources. Simulation results demonstrate that the proposed MBS and MiBS cooperative sensing scheme outperforms traditional single BS (MBS/MiBS) sensing scheme, showcasing superior sensing performance.

Increasing bandwidth demands of mobile video streaming pose a challenge in optimizing the Quality of Experience (QoE) for better user engagement. Multipath transmission promises to extend network capacity by utilizing multiple wireless links simultaneously. Previous studies mainly tune the packet scheduler in multipath transmission, expecting higher QoE by accelerating transmission. However, since Adaptive BitRate (ABR) algorithms overlook the impact of multipath scheduling on throughput prediction, multipath adaptive streaming can even experience lower QoE than single-path. This paper proposes Chorus, a cross-layer framework that coordinates multipath scheduling with adaptive streaming to optimize QoE jointly. Chorus establishes two-way feedback control loops between the server and the client. Furthermore, Chorus introduces Coarse-grained Decisions, which assist appropriate bitrate selection by considering the scheduling decision in throughput prediction, and Finegrained Corrections, which meet the predicted throughput by QoE-oriented multipath scheduling. Extensive emulation and real-world mobile Internet evaluations show that Chorus outperforms the state-of-the-art MPQUIC scheduler, improving average QoE by 23.5% and 65.7%, respectively.

Low-latency cloud rendering services use high-performance servers to provide mobile device users with exquisite graphics and convenient access experiences. Due to the complexity of the system and the diversity of impacting factors, identifying system bottlenecks has become a significant challenge. To demystify system performance, we build an online cloud rendering system to measure the latency distribution of its key components.

Many mobile devices are usually equipped with multiple interfaces, providing the opportunity of using multipath transport protocols such as Multipath TCP (MPTCP) to boost performance. The multipath congestion control algorithm (CCA) in MPTCP plays a vital role in achieving high performance and multipath fairness in mobile environments where the paths are often heterogeneous and dynamic. Such environments are very challenging for existing one-size-fits-all CCAs to achieve high performance while ensuring multipath fairness. In this paper, we present a novel framework, Disco, to dynamically select the most appropriate CCAs for MPTCP subflows at runtime according to the perceived network condition. Extensive experiments show that compared with existing multipath CCAs, the proposed solution can improve the average throughput by 19% – 25% and reduce the average queuing delay by up to 21 % while it barely does harm to multipath fairness.

This paper revisits the opportunities of using WiFi offloading for Vehicle to Internet (V2I) communication, and how this has changed over the last decade. With the rollouts of provider-managed WiFi networks that are more structured and operate under authenticated regimes, WiFi offloading, or use of available (roadside) WiFi networks for V2I data communication, has different opportunities and challenges. To study the current landscape,we develop a system (X-Fi), which efficiently selects, associates to, authenticates with, and performs WiFi offloading for V2I communication with these networks, and a tool (X-Perf), which illustrates opportunities of WiFi offloading available today in these networks, with measurements and experiments across four metro areas across three continents over 22 months. Our results indicate the feasibility of achieving 1 GB/hour application goodput, an order of magnitude higher than the number provided by open WiFi networks in the past, which can take a significant load away from alternative communication paths for V2I systems. Moreover, we provide several implications on transport protocols and WiFi deployments to shed light on the use of such WiFi networks for V2I communication.

We report XLINK, a multi-path QUIC video transport solution with experiments in Taobao short videos. XLINK is designed to meet two operational challenges at the same time: (1) Optimized user-perceived quality of experience (QoE) in terms of robustness, smoothness, responsiveness, and mobility and (2) Minimized cost overhead for service providers (typically CDNs). The core of XLINK is to take the opportunity of QUIC as a user-space protocol and directly capture user-perceived video QoE intent to control multi-path scheduling and management. We overcome major hurdles such as multi-path head-of-line blocking, network heterogeneity, and rapid link variations and balance cost and performance.