Video streaming service is one of the most popular applications for mobile users. However, mobile video streaming services consume a lot of energy, resulting in a reduced battery life. This is a critical problem that results in a degraded user’s quality of experience (QoE). Therefore, in this paper, a joint optimization scheme that controls both the central processing unit (CPU) and wireless networking of the video streaming process for improved energy efficiency on mobile devices is proposed. For this purpose, the energy consumption of the network interface and CPU is analyzed, and based on the energy consumption profile a joint optimization problem is formulated to maximize the energy efficiency of the mobile device. The proposed algorithm adaptively adjusts the number of chunks to be downloaded and decoded in each packet. Simulation results show that the proposed algorithm can effectively improve the energy efficiency when compared with the existing algorithms.

SUMMARYDynamic voltage and frequency scaling (DVFS) is an essential mechanism for power saving in smartphones and mobile devices. Central processing unit (CPU) load based DVFS algorithms are widely used due to their simplicity of implementation. However, such algorithms often lead to a poor response time, which is one of the most important factors of user experience, especially for interactive applications. In this paper, the response time is mathematically modeled by considering the CPU frequency and characteristics of the running applications based on the Linux kernel's completely fair scheduler (CFS), and a Response time constrained Frequency & Priority (RFP) control scheme for improved power efficiency of smartphones is proposed. In the RFP algorithm, the CPU frequency and priority of the interactive applications are adaptively adjusted by estimating the response time in real time. The experimental results show that RFP can save energy up to 24.23% compared to the ondemand governor and up to 7.74% compared to HAPPE while satisfying the predefined threshold of the response time in Android-based smartphones.

In this paper, a response time performance estimation scheme is proposed for smartphones applying dynamic voltage & frequency scaling (DVFS) at the central processing unit (CPU) and completely fair scheduler (CFS) at the Linux kernel. DVFS techniques influence the overall time performance of the smartphone since changes in CPU operational frequency will change all aspects of the CPUs responsiveness and access speed for task execution. CFS also has significant influence in task execution at the Linux kernel. Results show the effectiveness of the proposed scheme in estimating the instantaneous response time on a LG Optimus G smartphone.

The popularity of augmented reality (AR) applications and games are in high demand. Currently, the best common platform to implement AR services is on a smartphone, as online games, navigators, personal assistants, travel guides are among the most popular applications of smartphones. However, the power consumption of an AR application is extremely high, and therefore, highly adaptable and dynamic low power control schemes must be used. Dynamic voltage and frequency scaling (DVFS) schemes are widely used in smartphones to minimize the energy consumption by controlling the device's operational frequency and voltage. DVFS schemes can sometimes lead to longer response times, which can result in a significant problem for AR applications. In this paper, an AR response time monitor is used to observe the time interval between the AR image input and device's reaction time, in order to enable improved operational frequency and AR application process priority control. Based on the proposed response time monitor and the characteristics of the Linux kernel's completely fair scheduler (CFS) (which is the default scheduler of Android based smartphones), a response time step control (RSC) scheme is proposed which adaptively adjusts the CPU frequency and interactive application's priority. The experimental results show that RSC can reduce the energy consumption up to 10.41% compared to the ondemand governor while reliably satisfying the response time performance limit of interactive applications on a smartphone.