The increasing demand for high-definition live video streaming services on mobile devices is often hindered by unstable network conditions and limited computational capabilities. To address these issues, we introduce an adaptive video super-resolution (VSR) based mobile live streaming method, MOBLIVE. The core idea of MOBLIVE is to selectively offload regions of video frames that critically influence user perception quality to the server-side VSR model for enhancement. To further improve the video quality, we deploy a predictive model to choose the optimal VSR model for each selected region. Additionally, we employ an adaptive graphics processing unit (GPU) scheduling strategy that optimizes the allocation of multiple VSR tasks across multiple GPUs. Experimental results show that our approach outperforms the state-of-the-art method in video multi-method assessment fusion (VMAF) and reduces the latency by an average of 73.7% in the typical networking environment.

ARM TrustZone has become a cornerstone of security in mobile edge devices, but its protective measures often come at the expense of energy efficiency and system performance. Existing optimization methods rely heavily on cloud-based deep learning models and real-time measurements, making them vulnerable to fluctuating workloads and variable network conditions. This paper introduces a novel offline tuning framework for optimizing Trusted Applications (TAs) within the Open Portable Trusted Execution Environment (OP-TEE), an open-source TEE built on ARM TrustZone. Unlike traditional approaches, our method dispenses with on-device measurements and repeated TA compilations. Instead, it uses a predictive model trained on runtime characteristics observed in the Rich Execution Environment (REE) to estimate energy and performance metrics. Guided by these predictions, the framework then applies targeted optimizations through an automated tuning mechanism. Experimental results show that this offline approach accelerates the tuning process by 196× compared to the default method, while delivering a 27.23% performance improvement and a 25% reduction in energy consumption within just 170 s. These gains underscore the practicality and effectiveness of the proposed framework, paving the way for more efficient and adaptive TA optimization.

A trusted execution environment (TEE) is a system-on-chip and CPU system with a wide security solution available on today’s Arm application (APP) processors, which dominate the smartphone market. Generally, mobile APPs create a trusted application (TA) in the TEE to process sensitive information, such as payment or message encryption, which is transparent to the APPs running in the rich execution environments (REEs). In detail, the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA. Such an operation definitely increases the overhead of mobile APPs. In this paper, we first present a comprehensive analysis of the performance of open-source TEE encrypted text. We then propose a high energy-efficient task scheduling strategy (ETS-TEE). By leveraging the deep learning algorithm, our policy considers the complexity of TA tasks, which are dynamically scheduled between modeling on the local device and offloading to an edge server. We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server. The results show that compared with the default scheduling strategy on the local device, our approach achieves an average of 38.0 $\mathrm{\%}$ energy reduction and 1.6חspeedup. This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications, so that the trusted execution environment has both security and high performance.