Abstract-Optimizing the energy efficiency of mobile applications can greatly increase user satisfaction. However, developers lack viable techniques for estimating the energy consumption of their applications. This paper proposes a new approach that is both lightweight in terms of its developer requirements and provides fine-grained estimates of energy consumption at the code level. It achieves this using a novel combination of program analysis and per-instruction energy modeling. In evaluation, our approach is able to estimate energy consumption to within 10% of the ground truth for a set of mobile applications from the Google Play store. Additionally, it provides useful and meaningful feedback to developers that helps them to understand application energy consumption behavior.

The popularity of mobile apps continues to grow as developers take advantage of the sensors and data available on mobile devices. However, the increased functionality comes with a higher energy cost, which can cause a problem for users on battery constrained mobile devices. To improve the energy consumption of mobile apps, developers need detailed information about the energy consumption of their applications. Existing techniques have drawbacks that limit their usefulness or provide information at too high of a level of granularity, such as components or methods. Our approach is able to calculate source line level energy consumption information. It does this by combining hardware-based power measurements with program analysis and statistical modeling. Our empirical evaluation of the approach shows that it is fast and accurate.

No abstract

No abstract

An increasing amount of research has recently focused on representing affective states as continuous numerical values on multiple dimensions, such as the valence-arousal (VA) space. Compared to the categorical approach that represents affective states as several classes (e.g., positive and negative), the dimensional approach can provide more finegrained sentiment analysis. However, affective resources with valence-arousal ratings are still very rare, especially for the Chinese language. Therefore, this study builds 1) an affective lexicon called Chinese valence-arousal words (CVAW) containing 1,653 words, and 2) an affective corpus called Chinese valencearousal text (CVAT) containing 2,009 sentences extracted from web texts. To improve the annotation quality, a corpus cleanup procedure is used to remove outlier ratings and improper texts. Experiments using CVAW words to predict the VA ratings of the CVAT corpus show results comparable to those obtained using English affective resources.

Video streaming today accounts for up to 55% of mobile traffic. In this paper, we explore streaming videos encoded using Scalable Video Coding scheme (SVC) over highly variable bandwidth conditions such as cellular networks. SVC's unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop Layered Bin Packing (LBP) Adaptation Algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed including handling bandwidth prediction errors, and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared to the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today's mobile devices.

No abstract

Streaming videos over cellular networks is highly challenging. Since cellular data is a relatively scarce resource, many video and network providers offer options for users to exercise control over the amount of data consumed by video streaming. Our study shows that existing data saving practices for Adaptive Bitrate (ABR) videos are suboptimal: they often lead to highly variable video quality and do not make the most effective use of the network bandwidth. We identify underlying causes for this and propose two novel approaches to achieve better tradeoffs between video quality and data usage. The first approach is Chunk-Based Filtering (CBF), which can be retrofitted to any existing ABR scheme. The second approach is QUality-Aware Data-efficient streaming (QUAD), a holistic rate adaptation algorithm that is designed ground up. We implement and integrate our solutions into two video player platforms (dash.js and ExoPlayer), and conduct thorough evaluations over emulated/commercial cellular networks using real videos. Our evaluations demonstrate that compared to the state of the art, the two proposed schemes achieve consistent video quality that is much closer to the user-specified target, lead to far more efficient data usage, and incur lower stalls.