Energy-aware video streaming on smartphones

Abstract—Video streaming on smartphone consumes lots of energy. One common solution is to download and buffer future video data for playback so that the wireless interface can be turned off most of time and then save energy. However, this may waste energy and bandwidth if the user skips or quits before the end of the video. Using a small buffer can reduce the bandwidth wastage, but may consume more energy and introduce rebuffering delay. In this paper, we analyze the power consumption during video streaming considering user skip and early quit scenarios. We first propose an offline method to compute the minimum power consumption, and then introduce an online solution to save energy based on whether the user tends to watch video for a long time or tends to skip. We have implemented the online solution on Android based smartphones. Experimental results and trace-driven simulation results show that that our method can save energy while achieving a better tradeoff between delay and bandwidth compared to existing methods. I

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.Comment: To appear at IMC 201

Digital carbon footprint awareness among digital natives: an exploratory study

Changes in use practices due to COVID-19 have illustrated the potential of digital technology as a catalyst of more sustainable and pro-environmental behavior. At the same time, the energy consumption and environmental impact of digital applications and services has been put more firmly on the agenda. In this paper, we adopt a bottom-up approach to explore digital natives’ awareness of their digital carbon footprint, i.e., related to their use of digital services and applications. We present findings from 21 semi-structured in-depth interviews that were conducted to explore (1) to which extent digital natives are aware of and reflect on their digital carbon footprint, (2) what could motivate efforts to reduce this footprint and (3) which compromises they might be willing to make in this respect. The findings point to low awareness of the carbon footprint of digital applications and services. The lack of technological understanding, public information and social awareness about the topic were identified as important factors. In terms of the motivation for adopting pro-environmental digital habits, we found that several factors indirectly contribute to this goal, including the striving for personal wellbeing. Finally, the results indicate some willingness to change and make compromises, albeit not an unconditional one: the alignment with other goals (e.g., personal well-being) and nature of the perceived sacrifice and its impact play a key role. With this work, we aim to strengthen ongoing efforts to increase users’ awareness and to stimulate more sustainable and well-being supporting digital consumption

Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?

The increasing popularity of live video streaming from mobile devices, such as Facebook Live, Instagram Stories, Snapchat, etc. pressurizes the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of quality of experience (QoE) as the basis for network control, customer loyalty, and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users' quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: 1) adaptation; 2) energy efficiency; and 3) multipath content delivery. Discussions, challenges, and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided

Seamless multimedia delivery within a heterogeneous wireless networks environment: are we there yet?

The increasing popularity of live video streaming from mobile devices such as Facebook Live, Instagram Stories, Snapchat, etc. pressurises the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of Quality of Experience (QoE) as the basis for network control, customer loyalty and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users’ quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: adaptation, energy efficiency and multipath content delivery. Discussions, challenges and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided

Energy Awareness for Multiple Network Interface-Activated Smartphone

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 최성현.최신 스마트폰은 LTE와 Wi-Fi와 같은 네트워크 인터페이스 여러 개를 동시에 사용하여 전송율을 증가시키거나 네트워크 접속성을 향상시킬 수 있다. 이런 경우에, 스마트폰의 에너지 소모는 LTE와 Wi-Fi를 동시에 사용함에 따라 증가할 수 있다. 특히, 제한된 용량을 가지는 배터리로 동작을 하는 스마트폰에서 에너지 소모는 중요한 이슈이기 때문에, 에너지 증가와 성능 향상 사이의 trade-off를 고려하는 것이 요구된다. 에너지 인지 기술과 함께 스마트폰의 LTE와 Wi-Fi 인터페이스를 전략적으로 잘 사용함으로써, 배터리 에너지 소모를 줄임과 동시에 스마트폰 어플리케이션의 성능을 최적화 하는 것이 가능해진다. 본 논문에서는 LTE와 Wi-Fi 링크를 동시에 활용할 수 있는 스마트폰에서 에너지 인지를 가능하게 하는 다음 세 가지 전략을 고려하였다. (i) LTE와 Wi-Fi를 동시에 사용하는 스마트폰의 전력 소모 모델링, (ii) 스마트폰의 배터리 소모율의 실시간 예측 기법, (iii) dynamic adaptive streaming over HTTP (DASH) 기반의 비디오 스트리밍의 성능 최적화. 먼저, 동시에 여러 네트워크를 활성화하여 사용하는 스마트폰의 정밀한 전력 소모 모델링을 제시한다. 패킷 처리에 의해 소모되는 전력과 네트워크 인터페이스에서 소모되는 전력을 분해하여, 다중 네트워크 인터페이스를 활성화시키는 경우의 정밀한 전력 소모 모델을 구성한다. 다양한 시나리오에서 측정된 전력과 모델을 통해 예측된 전력을 비교하며 제안하는 전력 소모 모델의 정확성을 평가하였다. 기존 전력 소모 모델에 비해 단일 네트워크 통신의 경우에도 7%-35% 만큼 추정 오차를 줄였으며, 다중 네트워크 통신의 경우 추정 오차를 25% 줄임을 보였다. 두 번째로, 스마트폰의 실시간 에너지 인지를 가능하게 하기 위해서, 스마트폰에서 사용하고 있는 Li-ion 배터리의 특성을 고려하여 배터리 소모율을 추정하는 기법을 고안한다. Li-ion 배터리는 온도와 노화 상태에 따라서 가용 용량과 내부 저항이 달라지기 때문에, 온도와 노화 상태에 따라서 배터리 소모율이 달라질 수 있다. 배터리 특성을 모델링하는 것은 어려운 일이기 때문에, effective resistance 개념을 도입하여 용량과 내부 저항을 모르고도 배터리 소모율을 예측할 수 있는 BattTracker를 고안한다. BattTracker는 실시간 배터리 소모율을 최대 0.5초 마다 추정할 수 있다. 실제 스마트폰으로 다양한 실험을 통하여 BattTracker가 배터리 소모 예측을 5% 오차율 이내로 예측함을 보였으며, 이를 활용하면 높은 시간 해상도로 스마트폰의 에너지 인지 동작이 가능해진다. 마지막으로, 애너지 인지 기법과 LTE와 Wi-Fi 링크를 동시에 활용하는 것을 dynamic adaptive streaming over HTTP (DASH) 기반 비디오 스트리밍 어플리케이션에 적용한다. 스마트폰에서 LTE와 Wi-Fi 링크를 동시에 활용하는 것은 다양한 측면에서 DASH 기반의 비디오 스트리밍의 성능을 향상시킬 수 있다. 그러나, 배터리 에너지와 LTE 데이터 사용량을 절약하면서 끊김없는 고화질의 비디오를 스트리밍하는 것은 도전적인 일이다. 따라서, DASH 비디오를 시청하는 사용자의 Quality of Experience (QoE)를 향상시키기 위하여 LTE와 Wi-Fi를 동시에 활용하는 DASH video chunk 요청 기법인 REQUEST를 제안한다. REQUEST는 주어진 배터리 에너지와 LTE 사용량 예산 내에서 최적에 가까운 품질의 비디오를 끊김없이 스트리밍해 주는 것을 가능하게 한다. 다양한 환경에서의 시뮬레이션과 실 환경에서의 측정을 통하여, REQUEST가 기존 비디오 스트리밍 기법에 비해 평균 비디오 품질, 재버퍼링, 자원 낭비량 관점에서 상당히 우수함을 보인다. 요약하자면, 우리는 LTE와 Wi-Fi의 동시 사용하는 스마트폰에서의 전력 모델링 방법론, 실시간 배터리 소모율 추정 기법, DASH 기반 비디오 스트리밍 성능 최적화를 제안한다. 이 연구를 통해서, 우리는 프로토타입 구현과 실험 장비들을 통한 실측 기반으로 LTE와 Wi-Fi를 동시에 활용하는 스마트폰을 위한 에너지 인지 기술을 제안한다. 제안하는 기법들의 성능은 상용 스마트폰에 구현하여 실 환경에서의 성능 평가를 통해 검증하였다.State-of-the-art smartphones can utilize multiple network interfaces simultaneously, e.g., LTE and Wi-Fi, to enhance throughput and network connectivity in various use cases. In this situation, energy consumption of smartphones can increase while using both LTE and Wi-Fi interfaces simultaneously. Since energy consumption is an important issue for smartphones powered by batteries with limited capacity, it is required to consider the trade-off between energy increase and performance enhancement. By judiciously utilizing both LTE and Wi-Fi interfaces of smartphones and energy awareness techniques, it is enabled to optimize the performance of smartphones applications while saving battery energy. In this dissertation, we consider the following three strategies to enable the energy awareness for smartphones which utilize both LTE and Wi-Fi links: (i) Power modeling for smartphone which utilizes both LTE and Wi-Fi links simultaneously, (ii) real-time battery drain rate estimation for smartphones, and (iii) optimizing the performance of dynamic adaptive streaming over HTTP (DASH)-based video streaming for smartphones. First, an accurate power modeling is presented for the smartphones, especially, those capable of activating/utilizing multiple networks simultaneously. By decomposing packet processing power and power consumed by network interfaces, we construct the accurate power model for multiple network interface-activated cases. The accuracy of our model is comparatively evaluated by comparing the estimated power with the measured power in various scenarios. We find that our model reduces estimation error by 7%–35% even for single network transmissions, and by 25% for multiple network transmissions compared with existing power models. Second, in order to enable real-time energy awareness for smartphones, we develop a battery drain rate monitoring technique by considering characteristics of Li-ion batteries which are used by smartphones. With Li-ion battery, battery drain rate varies with temperature and battery aging, since they affect battery characteristics such as capacity and internal resistance. Since it is difficult to model the battery characteristics, we develop BattTracker, an algorithm to estimate battery drain rate without knowing the exact capacity and internal resistance by incorporating the concept of effective resistance. BattTracker tracks the instantaneous battery drain rate with up to 0.5 second time granularity. Extensive evaluation with smartphones demonstrates that BattTracker accurately estimates the battery drain rate with less than 5% estimation error, thus enabling energy-aware operation of smartphones with fine-grained time granularity. Finally, we adapt an energy awareness and utilize both LTE and Wi-Fi links for a Dynamic Adaptive Streaming over HTTP (DASH)-based video streaming application. Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of DASH-based video streaming in various aspects. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. Thus, we propose REQUEST, a video chunk request policy for DASH in a smartphone, which can utilize both LTE andWi-Fi to enhance users Quality of Experience (QoE). REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste. In summary, we propose a power modeling methodology, a real-time battery drain rate estimation method, and performance optimization of DASH-based video streaming for a smartphone which utilizes both LTE and Wi-Fi simultaneously. Through this research, we propose several energy-aware techniques for the smartphone, which especially utilizes both LTE and Wi-Fi, based on prototype implementation and the real measurement with experimental equipment. The performance of the proposed methods are validated by implementation on off-the-shelf smartphones and evaluations in real environments.1 Introduction 1 1.1 Motivation 1 1.2 Overview of Existing Approaches 3 1.2.1 Power modeling of smartphone 3 1.2.2 Battery drain rate estimation 4 1.2.3 Optimizing the performance of video streaming 5 1.3 Main Contributions 6 1.3.1 PIMM: Power Modeling of Multiple Network Interface-Activated Smartphone 6 1.3.2 BattTracker: Real-Time Battery Drain Rate Estimation 7 1.3.3 REQUEST: Performance Optimization of DASH Video Streaming 8 1.4 Organization of the Dissertation 8 2 PIMM: Packet Interval-Based Power Modeling of Multiple Network Interface-Activated Smartphones 10 2.1 Introduction 10 2.2 Background 12 2.2.1 Power saving operations of Wi-Fi and LTE 12 2.2.2 Related work 13 2.3 Power Consumption Modeling 14 2.3.1 Modeling Methodology 15 2.3.2 Packet interval-based power modeling 21 2.4 Practical issues 30 2.4.1 Impact of packet length 30 2.4.2 Impact of channel quality 33 2.5 Performance Evaluation 34 2.5.1 On-line power estimation 35 2.5.2 Single network data connections 37 2.5.3 Multiple network data connections 42 2.5.4 Model generation complexity 45 2.6 Summary 46 3 BattTracker: Enabling Energy Awareness for Smartphone Using Li-ion Battery Characteristics 47 3.1 Introduction 47 3.2 Background 50 3.2.1 Smartphones battery interface 50 3.2.2 State of Charge (SoC) and Open Circuit Voltage (Voc) 50 3.2.3 Batterys internal resistance 53 3.2.4 Related Work 53 3.3 Li-ion Battery Characteristics 54 3.3.1 Impact of temperature and aging on Li-ion battery 54 3.3.2 Measuring battery characteristics 54 3.3.3 Battery characteristics models 57 3.4 Effective Internal Resistance 58 3.4.1 Effective internal resistance (re) 58 3.4.2 Battery drain rate and lifetime derived from re 60 3.5 BattTracker Design 61 3.5.1 Rd estimator 62 3.5.2 Voc estimator 62 3.5.3 re estimator 64 3.6 Performance Evaluation 65 3.6.1 Comparison schemes and performance metrics 66 3.6.2 Convergence time of re 67 3.6.3 Power consumption overhead of BattTracker 67 3.6.4 Comparison with measurement using equipment 68 3.6.5 BattTracker with aged batteries 70 3.6.6 BattTracker with various video applications 70 3.6.7 BattTracker with varying temperature 73 3.7 Summary 74 4 REQUEST: Seamless Dynamic Adaptive Streaming over HTTP for Multi-Homed Smartphone under Resource Constraints 75 4.1 Introduction 75 4.2 Background and Related Work 77 4.2.1 Background 77 4.2.2 Related Work 79 4.3 Motivation 80 4.3.1 Wi-Fi Throughput Fluctuation 80 4.3.2 Optimizing Resource Utilization 82 4.4 Proposed Chunk Request Policy 83 4.5 Problem Formulation 86 4.6 REQUEST Algorithm 90 4.6.1 Request Interval Adaptation 91 4.6.2 Chunk Request Adaptation 94 4.7 Performance Evaluation 96 4.7.1 Prototype-Based Evaluation 98 4.7.2 Trace-Driven Simulation 102 4.8 Summary 104 5 Concluding Remarks 106 5.1 Research Contributions 106 5.2 Future Work 107 Abstract (In Korean) 117Docto