This article proposes a novel energy-efficient multimedia delivery system called EStreamer. First, we study the relationship between buffer size at the client, burst-shaped TCP-based multimedia traffic, and energy consumption of wireless network interfaces in smartphones. Based on the study, we design and implement EStreamer for constant bit rate and rate-adaptive streaming. EStreamer can improve battery lifetime by 3x, 1.5x, and 2x while streaming over Wi-Fi, 3G, and 4G, respectively.
Many papers have also studied the energy efficiency of 3G communication. xiao08youtube were among the first to study the energy consumption of YouTube streaming over both Wi-Fi and 3G. We go much beyond the scope of that work by considering the impact of traffic shaping and different network configurations, including LTE. Afterwards, balasubramanian09imc3g performed a measurement study on the energy consumption of 3G communication but did not consider streaming applications in their study. qian11mobisys characterized the energy efficiency of several different applications. They observed that some music streaming applications behave in an energy inefficient manner due to the CBR traffic. Earlier in @cite_7 , the same authors proposed a traffic shaping scheme for YouTube and compute estimates on potential energy savings with that scheme. However, they did not consider the consequence of TCP flow control on the energy consumption of WNIs in their study. A more recent and thorough measurement study on different mobile video streaming services and the resulting energy consumption on different mobile OSs and devices is presented in @cite_11 .
@cite_7 @cite_11
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3G cellular data networks have recently witnessed explosive growth. In this work, we focus on UMTS, one of the most popular 3G mobile communication technologies. Our work is the first to accurately infer, for any UMTS network, the state machine (both transitions and timer values) that guides the radio resource allocation policy through a light-weight probing scheme. We systematically characterize the impact of operational state machine settings by analyzing traces collected from a commercial UMTS network, and pinpoint the inefficiencies caused by the interplay between smartphone applications and the state machine behavior. Besides basic characterizations, we explore the optimal state machine settings in terms of several critical timer values evaluated using real network traces. Our findings suggest that the fundamental limitation of the current state machine design is its static nature of treating all traffic according to the same inactivity timers, making it difficult to balance tradeoffs among radio resource usage efficiency, network management overhead, device radio energy consumption, and performance. To the best of our knowledge, our work is the first empirical study that employs real cellular traces to investigate the optimality of UMTS state machine configurations. Our analysis also demonstrates that traffic patterns impose significant impact on radio resource and energy consumption. In particular, We propose a simple improvement that reduces YouTube streaming energy by 80 by leveraging an existing feature called fast dormancy supported by the 3GPP specifications. Multimedia streaming applications are among the most energy hungry applications in smartphones. The energy consumption mostly depends on the delivery techniques and on the power management techniques of wireless interfaces (Wi-Fi and 3G). In order to provide insights on what kind of streaming techniques exist, how they work on different mobile platforms, and what is their impact on the energy consumption of mobile phones, we have done a large set of active measurements with several smartphones having both Wi-Fi and cellular network access. Our analysis reveals five different techniques to deliver the content to the video players. The selection of a technique depends on the device, player, quality, and service. The results from our power measurements allow us to conclude that none of the identified techniques is optimal because they take none of the following facts into account: access technology used, user behaviour, and user preferences concerning data waste. However, we point out the techniques that provide the most attractive trade-offs in particular situations. Furthermore, we make several observations on the energy consumption of different players, containers, and video qualities that should be taken into consideration when optimizing the energy consumption.