Nevertheless, the proliferation of mobile computing in general, and even more specifically of mobile video streaming, is hindered by the physical constraints and limitations of the underlying hardware. One key issue in this regard is related to one of the most critical resources of a mobile device – its battery. Mobile video streaming applications are among the most power-hungry smartphone apps [[17]](#CR17) and the intensive growth in the amount of mobile video streaming data continues to put significant pressure on the power consumption of smart mobile devices [[55]](#CR55). At the same time, the battery technology is experiencing a disproportionally slower growth – practically a stagnation — compared to the other mobile resources including the CPU speed and computing power, storage space, and wireless transmission speed [[13]](#CR13). The lack of a revolutionary solution for modest battery capacity calls for further efforts towards the efficient use of limited resources available on mobile devices. Inspired by approximate mobile computing (Section 2.1), in this work we aim to investigate the feasibility of implementing context-, content-, and user-dependent video quality adaptation with the goal of improving the energy efficiency of mobile video playback. 1 While building upon the general idea of [[25]](#CR25)], in the current manuscript we greatly expand this research by thoroughly investigating how spatial and temporal properties of the video modulate the relationship between the desired resolution and a user’s physical activity. Furthermore, we for the first time examine the role of a user’s personality aspects on the mobile video resolution requirements. The additional investigations are conducted through a separate user study with 22 users who had not participated in the original study. Finally, we fully revise the statistical methodology that now includes sophisticated hierarchical modeling of the target relationships. Our work is driven by the following hypotheses: 1. Video playback resolution represents a suitable “knob” for trading off video playback quality and the corresponding energy usage; 2. A viewer’s requirements with respect to the video playback quality vary with the physical context (i.e. the activity state) of the viewer; 3. A viewer’s requirements with respect to the video playback quality depend on the content-related properties of the video; 4. Subjective factors pertaining to the viewer may influence the required video quality. 5. Significant energy can be saved by adapting the playback resolution according to the minimal level that still satisfies a user’s quality expectations. We start by performing fine-grain energy measurements in order to profile the role of video playback resolution on the total energy consumption of a mobile device (Section 2.2). We then conduct two studies, described in Section 3, to examine the remaining three assumptions. The first study is targeted at investigating the influence of contextual situations (such as whether a user is still, running, walking, or riding in a car) on the video quality requirements. The study confirms that these factors significantly impact the minimum playback resolution the user is satisfied with. In addition to this, findings further examined in Section 4 uncover other aspects that can also play a role in the user’s tolerance of lower video quality, such as the video’s content (described by its spatial and temporal complexity) and user-related factors, confirming the last two assumptions. Building upon these initial findings, we design the second study more rigorously targeted to investigate the impact of the video’s spatial and temporal characteristics on the required playback quality. In addition, we also examine other human factors that could influence user quality expectations, such as the user’s personality traits. Thus, in the second study we also collect information on the personality of the participants, more in-depth information about the properties of the video content, and employ a more rigorous statistical analysis based on mixed linear models. Our investigation clearly pinpoints the physical activity, but also the interplay between the physical activity and the video content, as well as the impact of personality and gender-related factors on the opportunities for reducing the mobile video energy requirements through controlled approximation. The novelty of our work stems not only from identifying contextual factors that impact the viewing requirements, but also from devising the predictive models (Section 4.5) that would enable real time inference of the minimal desired viewing resolution. Merging mobile systems design, mobile sensing, and human-computer interaction, our work opens a new space for dynamic minimization of the gap between the users needs and the computational effort delivered by mobile computers. The contextual information, including a viewer’s mobility state, properties of the video content, and even personality traits, that we focus on in our studies, is deliberately selected as it may be acquired with very little cost/overhead in today’s ubiquitous mobile devices and apps. Therefore, our work remains readily implementable in practice, providing a new dimension to the existing, mostly statically applied, approaches to resource-efficient multimedia described in Section 5. The implications of our research focus on bridging the gap between what a user can really process/perceive from the multimedia content which is being played and the actual QoS (Quality of Service) delivered by the multimedia application. This enables energy savings for existing mobile multimedia applications by exploiting information that is already available on a mobile device (e.g. the physical activity of the user), thus extending the already very constrained battery capacity on such devices. At the same time, our work has important implications for the existing and future pervasive ambient displays, such as embedded displays and multi-touch surfaces, flying (on-drone) displays, wearable and flexible displays, and head-mounted AR/VR displays. Our solution can foster its context-aware adaptation to enable energy efficient operation of these displays by adjusting the QoS to the different contextual situations and the particularities of the user(s). In Section 7 we discuss future research avenues in the area of mobile video adaptation, but also in the area of approximate mobile computing in general. ## Background & preliminaries ### Towards approximate mobile computing Approximate computing (AC) is a resource-efficient computing paradigm grounded in the observation that the result of a computation often need not be perfectly accurate to satisfy the end-user’s needs [[29]](#CR29)]. Opportunities for AC frequently arise when the computation inputs are noisy (e.g. sensor data), or when the output is further manipulated and interpreted by the user (e.g. augmented reality rendering). In such situations, approximate computation can deliver a fully satisfactory result while reducing the energy use. AC techniques have already proven their efficiency in various desktop scenarios, with approaches ranging from speeding up code execution through compiler-level optimizations that omit certain lines of code [[23]](#CR23)] to performing neural-network based approximations instead of complex function calculations [[12]](#CR12)], demonstrating significant energy savings while maintaining acceptable result accuracy. Building upon the idea of AC, approximate mobile computing (AMC) introduces approximation on mobile devices [[33]](#CR33)]. The core difference from the conventional AC being the context of use, which in mobile computing tends to vary over time. A user’s physical activity, location and collocation with other users, the outside brightness, and numerous other factors may vary throughout the day and impact the user’s requirements with respect to mobile computation. Significant challenges lay ahead before the full potential of AMC can be exploited: 1) practical means of enabling approximation in mobile apps need to be provided; 2) the benefits of approximate execution need to be quantified; 3) opportunities for approximation need to be identified and profiled, and 4) lightweight context recognition relevant for AMC needs to be implemented. This paper describes our efforts towards enabling AMC in the field of mobile video playback. This field represents not only one of the most prominent aspects of mobile computing, but is also among the most energy hungry ones [[50]](#CR50)]. We hypothesize that the context of the mobile video playback impacts the user’s perception and quality requirements. By “context” one can understand a potentially unlimited number of dimensions, however, backed by the prior work [[39]](#CR39), [42]](#CR42), [45]](#CR45)] in our experiments we focus on the three most relevant and intuitive dimensions – a user’s physical activity, the characteristics of the mobile video, and the user’s personality traits. In addition, we are interested in the potential of enabling energy savings by adjusting video playback according to the current context. Consequently, we formulate the following research questions (RQ) that our study aims to answer: - _RQ_ _1_ _: Does setting the video playback resolution on a mobile device enable a trade-off between the energy usage and the video rendering quality?_ - _RQ_ _2_ _: Does the physical activity the user is engaged in when watching a video on a mobile device influence the user’s quality expectations/requirements?_ - _RQ_ _3_ _: Does the video content (its spatial and temporal characteristics) impact the user’s satisfaction with a given video playback quality and does the physical mobility state of the user modulate the relationship between the video content properties and the desired playback quality?_ - _RQ_ _4_ _: Do the user’s personality traits impact the quality requirements of a mobile video playback?_ To realize AMC the first step is to provide straightforward and efficient means of adjusting approximation. In addition, the reduction in computations (e.g. decreased resolution) should lead to a gradual decrease in the end-result accuracy (e.g. user quality perception), without the loss of correctness (i.e. the result is usable at all times, and the approximation “knob” always gives a correct result). Moreover, the reduction in computation should translate to reduced resource usage (and thus energy savings). In our work we settle on _video decoding resolution_ adjustment. Virtually all video distribution frameworks (e.g. Youtube, Vimeo), as well as mobile video players, support playback resolution adaptation. Furthermore, setting video resolution always leads to correct execution and the loss of quality is gradual as we dial down the resolution. In the following section we also confirm that the loss of quality corresponds to lower resource usage making video decoding resolution a suitable technique for approximate computing adaptation. Our work takes one step further the previous research efforts in the field of energy-efficient mobile multimedia, which include solutions that mostly focus on optimizations at the hardware and network layer for video streaming such as: energy aware CPU scaling [[18]](#CR18)], battery-aware streaming rate adaptation [[1]](#CR1)] or dynamic voltage and frequency scaling [[24]](#CR24)]. Compared to the existing solutions, we propose a context- and content aware, hardware-agnostic approach with applicability for both network video streaming and on-device playback. ### Energy vs. Quality trade-off in mobile video decoding The approximate computing philosophy has at its core the monotonically increasing relationship between the computation accuracy and the resource consumption. In this section we chart the relationship between the video decoding quality and the mobile consumption. When performing the energy measurements, we use a popular video decoding software VLC Player [[46]](#CR46)] running on a Samsung Galaxy S3 (I9300) Android smartphone. Despite being released nine years ago, the phone supports both hardware and software video decoding and, importantly, has a detachable battery that allows us to connect the phone to a high-frequency power meter. The VLC Player was chosen for the energy measurements due to its flexibility in allowing rapid enabling/disabling of hardware accelerated decoding. The experimental setup for measuring energy consumption relies on measurements from the Monsoon High Voltage Power Monitor (HVPM) [[30]](#CR30)], a high sampling frequency platform commonly used for power measurements in mobile computing [[36]](#CR36)]. This platform generates energy readings at a sampling frequency of 5 _k_ _H_ _z_. Each sample contains a timestamp in _ms_, voltage in _m_ _V_ and electrical current in _m_ _A_. The HVPM is directly attached to the battery interface of the mobile device, which is powered solely by the HVPM. During the energy measurements, the HPVM output voltage was set to 4.2 _V_ corresponding to the voltage of an almost full battery. The same 1-minute video was downloaded from YouTube on the device in the following resolutions: 144p, 240p, 360p, 480p, 720p and 1080p, in both WebM and MPEG-4 formats. The baseline for comparison was a reference energy measurement performed with just the phone screen turned on, without other apps/services running. For each resolution, the video was played 10 times using VLC Player and the energy readings were averaged over the 10 runs. During the measurements, the screen brightness was set to the minimum, all non-essential services running on the smartphone that could interfere with the energy measurements were shut down, and the smartphone’s Airplane mode was turned on to avoid the effect of on-device communication modules (e.g. GSM, Wi-Fi, Bluetooth, etc.). The results of the energy measurements for video playback on the mobile device at different resolutions are shown in Fig. 1 (for the reference we also show the measurements with the screen turned on, but no playback running). We observe a significant difference in power consumption for playing videos using MPEG-4 vs. WebM decoding. This is expected since MPEG-4 decoding is hardware-accelerated in modern smartphones, while WebM decoding is performed in software. With both formats we see a generally increasing trend – the higher the decoding quality (resolution), the higher the consumption is. Interestingly, in the WebM case the lower resolutions (144p, 240p and 360p) have similar average current consumption, while the consumption increases considerably as we move to higher resolutions (480p, 720p and finally 1080p). Since there are no significant differences between the lower three resolutions, from the energy efficiency point of view, lowering the resolution under 360p would have no positive impact on energy savings, moreover it would only potentially decrease a user’s satisfaction. #### Fig. 1. !Fig. 1 Open in a new tab Smartphone average current consumption during video playback at different resolutions together with the standard deviation of the measurements. A monotonically increasing relationship between the video decoding resolution and the current consumption is evident for both software (WebM) as well as hardware (MPEG 4) decoding