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.
Traffic shaping is widely used to save communication energy for wireless multimedia streaming @cite_14 . 9.wang proposed an adaptive streaming technique for the UDP-based multimedia streaming. The system works at the server or proxy and manipulates the burst interval depending on the packet loss and network situation experienced by the streaming client. On the other hand, our focus is HTTP over TCP-based streaming. We modeled the energy consumption of bursty TCP traffic. Then we developed EStreamer based on the models. EStreamer depends on standard TCP properties to apply traffic shaping, which greatly simplifies the implementation of energy aware streaming. It does not require either the support of any secondary protocol such as RTCP or the modification of TCP or any other protocols. Therefore, EStreamer can be easily integrated with modern TCP-based streaming services.
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2044762744 2045258279 2017146017 2335146458
Shaping constant bit rate traffic into bursts has been proposed earlier for UDP-based multimedia streaming to save Wi-Fi communication energy of mobile devices. The relationship between the burst size and energy consumption of wireless interfaces is such that the larger is the burst size, the lower is the energy consumption per bit received as long as there is no packet loss. However, the relationship between the burst size and energy in case of TCP traffic has not yet been fully uncovered. In this paper, we develop a power consumption model which describes this relationship in wireless multimedia streaming scenarios. Then, we implement a cross-layer stream delivery system, EStreamer. This system relies on a heuristic derived from the model and on client playback buffer status to determine a burst size and provides as small energy consumption as possible without jeopardizing smooth playback. The heuristic greatly simplifies the deployment of EStreamer compared to most existing solutions by ensuring energy savings regardless of the wireless interface being used. We show that in the best cases using EStreamer reduces energy consumption of a mobile device by 65 , 50-60 and 35 while streaming over Wi-Fi, LTE and 3G respectively. Compared with existing energy-aware applications energy consumption can be reduced by 10-55 further. While the 802.11 power saving mode (PSM) and its enhancements can reduce power consumption by putting the wireless network interface (WNI) into sleep as much as possible, they either require additional infrastructure support, or may degrade the transmission throughput and cause additional transmission delay. These schemes are not suitable for long and bulk data transmissions with strict QoS requirements on wireless devices. With increasingly abundant bandwidth available on the Internet, we have observed that TCP congestion control is often not a constraint of bulk data transmissions as bandwidth throttling is widely used in practice. In this paper, instead of further manipulating the trade-off between the power saving and the incurred delay, we effectively explore the power saving potential by considering the bandwidth throttling on streaming downloading servers. We propose an application-independent protocol, called PSM-throttling. With a quick detection on the TCP flow throughput, a client can identify bandwidth throttling connections with a low cost Since the throttling enables us to reshape the TCP traffic into periodic bursts with the same average throughput as the server transmission rate, the client can accurately predict the arriving time of packets and turn on off the WNI accordingly. PSM-throttling can minimize power consumption on TCP-based bulk traffic by effectively utilizing available Internet bandwidth without degrading the application's performance perceived by the user. Furthermore, PSM-throttling is client-centric, and does not need any additional infrastructure support. Our lab-environment and Internet-based evaluation results show that PSM-throttling can effectively improve energy savings (by up to 75 ) and or the QoS for a broad types of TCP-based applications, including streaming, pseudo streaming, and large file downloading, over existing PSM-like methods. Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS), which brings two new features. First, by using HTTP TCP, it leverages network-friendly TCP to achieve both firewall NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom in HAS design is that since the TCP throughput observed by a client would indicate the available network bandwidth, it could be used as a reliable reference for video bitrate selection. We argue that this is no longer true when HAS becomes a substantial fraction of the total network traffic. We show that when multiple HAS clients compete at a network bottleneck, the discrete nature of the video bitrates results in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to video bitrate oscillation and other undesirable behaviors that negatively impact the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation (where "probe" refers to trial increment of the data rate, instead of sending auxiliary piggybacking traffic), which is akin, but also orthogonal to the transport-layer TCP congestion control. We present PANDA - a client-side rate adaptation algorithm for HAS - as a practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75 without increasing the risk of buffer underrun. Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.