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# Experimental Analysis of Energy Consumption in Video Streaming Services

Youssef Badra, Razvan Stanica

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Youssef Badra, Razvan Stanica. Experimental Analysis of Energy Consumption in Video Streaming Services. WoWMoM 2025 - 26th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, May 2025, Fort Worth (TX), United States. hal-05095936

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https://inria.hal.science/hal-05095936v1

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# Experimental Analysis of Energy Consumption in Video Streaming Services

Youssef Badra, Razvan Stanica

INSA Lyon, Inria, CITI, F-69621 Villeurbanne, France

*Abstract*—With more mobile devices in the world than the global population and with growing environmental concerns, the energy consumption of mobile devices is becoming an important topic. At the same time, video streaming represents the most popular service on the Internet in terms of traffic volume, and video content is increasingly consumed by users on mobile devices. Therefore, in this study, we design and conduct an experimental campaign to measure the energy consumption of mobile devices and their wireless network modules in the context of video streaming services. We compare the energy consumption for five video streaming platforms over multiple radio access technologies (WiFi, 3G, 4G, and 5G), using commercial networks as well as a private cellular network set-up. Our results show a significant variation in terms of energy consumption between video platforms, with very different behaviors appearing at the inspection of the collected traces. We also show that the 5G technology represents, at least for now, the most energy-hungry solution at the user level. Finally, we test the different video quality options available for the different platforms, showing once again a very heterogeneous behavior in terms of power consumption patterns.

## I. INTRODUCTION

Mobile networks have become ubiquitous in our society, with a number of subscribers surpassing the entire world population [1]. The total global mobile world traffic reached 130 EB per month in 2023, with video content representing 73% of this number [1]. At the same time, new services with challenging networking demands, such as extended reality (XR), are entering the market and they are expected to trigger a further increase in mobile traffic demand before the end of the decade [1]. In this context, the energy consumption of user equipment (UE) plays a major role, for two main reasons. First of all, with the information and communication technologies (ICT) sector now representing 4% of the worldwide energy consumption [1], its contribution to global CO2 emissions can no longer be neglected. While the radio access network (RAN) is generally considered the main contributor of the sector in terms of energy consumption, the 8.5 billion mobile devices actually add up to a bigger overall impact [2]. Second, with UE generally battery-powered, a certain minimum energy autonomy is required for users to enjoy the expected experience. With more complex and immersive applications being developed, understanding and reducing the energy consumption on mobile devices becomes an important objective.

Measuring UE energy consumption can be conducted using software probes [3], or hardware probes [4], with the second being considered more accurate. However, hardware measurement campaigns in the literature [4], [5] present two

significant shortcomings. First, traditional power consumption measurements are typically taken at the battery connector, which provides the overall power usage of the entire device. This method captures the total UE energy consumption, hence it can obscure the specific power usage of individual components, such as the radio frequency (RF) module. This is because the total power reading includes, in this case, the consumption of all device components, such as the screen, the processor, the memory, etc. Such a measurement setup can lead to multi-layered power patterns that mask the specific energy consumption of the RF module. The second problem comes from the fact that whenever the RF module is isolated and its energy consumption measured [6], this is done for very specific (and rather exotic) smartphone models.

In our work, we develop a methodology that allows us to conduct an energy measurement campaign on well-known commercial devices. The iPhone X and iPhone 12 Mini, used in this study, have over 150 million units sold. Moreover, as explained, video content represents the major source of mobile data traffic, which pushes us to study specifically video streaming services. Therefore, these two types of UE serve as the basis for our study on power consumption across various video streaming platforms. By leveraging these popular smartphone models, we aim to provide accurate measurements of power consumption and examine the impact of different RAN technologies, application behaviors, and streaming quality.

To ensure real-world relevance, we conducted tests using customized smartphones on popular commercial streaming platforms. Our extensive dataset includes over 50 hours of streaming sessions across various devices. We analyzed the effects of five distinct RAN technologies on both commercial networks provided by Orange and a private cellular network. These technologies include Wi-Fi (2.4 GHz and 5 GHz), 3G, 4G, and 5G non-standalone (NSA). The comprehensive scope of our experimental setup sets our research apart from previous studies, offering a thorough view of power consumption in practical conditions. Our measurement approach focuses on isolating the power consumption of the RF module by modifying the hardware of the devices. This method reduces inaccuracies caused by multi-layered power patterns, allowing for a precise evaluation of the RF module energy use across various network scenarios and streaming activities.

Our methodology allows us to take physical power measurements at the component level on real smartphone hardware, ensuring the devices remain fully functional during
the measurement process. By examining iPhone 12 Mini and
iPhone X models, which support 3G, 4G, and 5G sub-6 GHz,
and operate under iOS 17.4.1 and iOS 16.7.7, respectively,
we profiled the power consumption of these technologies
in the case of video streaming services. By using popular
video streaming applications such as Netflix, YouTube, Apple
TV, Disney+, and Amazon Prime Video on the latest iOS
versions and leveraging the widespread commercial success
of these iPhone models, along with testing across a diversity
of wireless technologies, on both commercial and private op-
erators, our measurements accurately reflect real-world usage
scenarios.

While many studies rely on software profiling and in-
ternal sensors, our research employs hardware-level power
measurement techniques using modified commercial mobile
devices. This method ensures precise and detailed data on
the energy consumption of the wireless network modules
during video streaming, offering significantly higher accuracy
than software-based approaches. By isolating and focusing on
the RF module in real-world scenarios, our study provides
a detailed and reliable analysis of power consumption, con-
tributing valuable insights for enhancing energy efficiency in
mobile video streaming. Indeed, by investigating the power
consumption patterns associated with video streaming, the
most popular mobile service, our work offers valuable insights
for researchers and developers seeking to enhance mobile
energy efficiency. By exploring the interplay between technol-
ogy, application behavior, and streaming quality, we provide
actionable guidance for optimizing power consumption in
mobile devices.

In summary, our research offers a comprehensive exami-
nation of power consumption across various video streaming
platforms and network technologies, using precise hardware-
level measurements. By analyzing real-world scenarios, we
aim to guide future optimizations in mobile device power
usage, ultimately contributing to more energy-efficient and
sustainable mobile technology practices. In the following, Sec-
tion II discussed related works. We describe our measurement
platform in Section III and detail our metrology results in
Section IV. Concluding remarks can be found in Section V.

II. RELATED WORKS

The energy consumption of mobile devices has become
a focal point of research, with numerous studies assessing
the impact of various applications and system-level activities.
Shye et al. [5] were among the first to present methods for
studying the power consumption of mobile devices in real-
world usage. They develop a power estimation model and
demonstrate power optimizations that leverage user activity
patterns to achieve more than 10% energy savings with
minimal impact on user satisfaction. In a larger scale study
modeling study, Chen et al. [7] analyze smartphone energy
drain in natural settings and develop a hybrid power model.
This allowed them to analyze the energy drain of smartphones
in the wild using traces collected from 1520 devices. They

show that 45% of total daily energy drain occurs during
screen-off periods, with a significant portion coming from
cellular paging, WiFi beacons, and WiFi scanning.

Of course, the limits of these studies come from the fact
that the proposed models are coming from a limited number
of measurements, on specific devices, and they can hardly
be generalized. The work in [6] takes a different approach,
providing a detailed analysis of the power consumption of a
specific smartphone model, the Openmoko Neo Freerunner.
The authors measure the power consumption of its major
components under various usage scenarios, in order to enable
effective energy management. They find that the majority of
power consumption in the smartphone is attributed to the GSM
module and the display subsystem. In our work, we also
employ a hardware-based power consumption measurement
approach, which is known to be a very accurate and reli-
able method of measurement. This approach involves taking
physical power measurements at the component level on real
smartphone hardware under real-life usage scenarios, while
ensuring the device is functional during the measurement
process. However, in our case, we conduct this measurement
on widely used smartphone models.

Previous research has extensively explored different facets
of the energy consumption associated with video streaming
platforms. For instance, [8] proposes an analytical power con-
sumption model for hardware-accelerated H.264/AVC video
decoding on mobile platforms and a solution for power-rate-
optimized mobile video streaming to maximize video quality
given limited network bandwidth and battery life. Another
work [9] investigates the impact of wireless link quality
and network load on the energy consumption of an Android
device during video streaming and analyzes the benefits of
using adaptive multimedia mechanisms to save energy while
maintaining acceptable user-perceived quality. In their results,
decreasing the video quality level can achieve significant
energy savings, up to 50% when switching from the highest
quality to medium quality, with only a moderate decrease in
user-perceived quality. Another finding is that using TCP as
the transport protocol is more energy efficient than using UDP
in all scenarios.

In contrast to prior investigations that concentrated on
individual platforms or specific streaming protocols or codecs,
our study offers a thorough comparative examination of five
prominent video streaming platforms (Amazon, Apple TV,
Disney, Netflix, and YouTube). This methodology enables us
to discern energy usage patterns that are specific to each
platform on mobile devices.

III. MEASUREMENT SETUP

Smartphone energy consumption can be measured using
either software or hardware approaches [7]. However, software
probes [3] can not accurately measure the energy consumption
of the different smartphone components while, in our case,
we want to focus on the energy consumption of the wireless
network component (RF module) in the context of video
streaming services.

Therefore, in order to conduct precise energy consumption
measurements at the UE level, we build an experimental plat-
form containing three parts: hardware-modified smartphones,
a power monitoring instrument, and a data acquisition unit.
On the smartphone side, we use two different devices: an
iPhone 12 Mini A2399 and an iPhone X A1865. These specific
models are popular commercial smartphones, largely used
today, and they were selected in this study for reasons that
will be detailed below. For power monitoring, we use a PAC-
1954 power monitor sensor. This is a quad power and energy
monitor with 16-bit voltage and current sensing resolution and
a maximum sampling rate of 1024 Hz. This power monitoring
sensor is installed on a Mikroe Fusion development board
with an ARM STM32F407 microcontroller. Finally, the data
acquisition unit is a Linux computer that is connected to
a power monitor instrument via serial communication. The
data acquisition unit sends the sensed data to the host at a
sample rate of about 64 samples per second, for two different
power sensing channels. This measurement setup is depicted
in Fig. 1.

<figure><img src="image_3.png" /><figcaption>Fig. 1: Energy consumption measurement platform.</figcaption></figure>

Previous works using hardware energy measurement
probes [5], [8] focus on the battery drain, practically mea-
suring the energy consumption of the entire smartphone. In
this work, we aim to improve the accuracy of such energy con-
sumption measurements by assessing the power consumption
of the wireless connectivity components only under the most
network-intensive popular service, namely video streaming.

However, modern smartphones have complex internal archi-
tecture and multi-layered power consumption patterns. More-
over, the internal smartphone architecture differs for differ-
ent manufacturers, or even for different smartphone models.
Therefore, our work began by identifying smartphone models
with hardware allowing us to separate the power consumption
of the RF module from the overall power consumption of
the smartphone. By modifying these smartphones, we can
measure the consumption of the RF components (such as
transceivers, baseband processors, power amplifiers, etc.) and
eliminate the consumption of the screen, camera, processor,
memory, etc.

Indeed, these non-network components represent a signifi-
cant portion of smartphone power consumption, and they add
important noise to any analysis focused on the networking

<figure><img src="image_4.png" /><figcaption>Fig. 2: Power consumption for an iPhone 12 mini on a 4G network during 15 minutes of video streaming: P<sub>RF</sub> power consumed by the RF module, and P<sub>BAT</sub> overall power consumed by the smartphone.</figcaption></figure>

part. To demonstrate this, in Fig. 2, we show a comparison
between the overall energy consumed by the smartphone and
the energy consumption of the RF module, during a 15-
minute video streaming session. We notice that consumption
peaks in the overall measurement are generally related to
network operations (transmissions and receptions), but the
overall energy consumption is dominated by other smartphone
elements. Since our goal is to analyze the energy consumption
of network functions, this clearly motivates us to propose
a measurement setup capable of separating these different
components. With our design, we can conduct measurements
on the RF module of commercial smartphones in real-world
scenarios, and we are able to detect and analyze the most
power-hungry networking operations of the smartphone.

As detailed below, to accomplish this, we began by se-
lecting smartphones with an internal logic board architecture
that allows these kinds of modifications and measurements. Of
course, we still needed to overcome the challenges related to
the internal electronics. The intricate nature of the electronic
components on logic boards makes them too small to be seen
without a microscope. Lastly, to realize our final setup, we
need to make modifications to some traces, pads, vias, and
component footprints of the logic printed circuit board (PCB).

A. Hardware modifications

As we want to obtain accurate energy measurements for a
specific smartphone function (wireless connectivity), we pre-
fer hardware probes to software ones. Moreover, our interest in
networking functions implies that we need to be more precise
than simply measuring the battery drain. For this, we bring
some hardware modifications to specific smartphone models,
with the goal of gaining access to the internal power rail of
the logic board. Of course, we want to achieve this objective
without adding any component that could have an impact on
the power consumption we desire to measure.

The choice of smartphones capable of supporting such
hardware modifications is complex. In this study, we used
two smartphones: the iPhone 12 mini A2399 EU version,
which can access 4G, 5G NSA sub6GHz, Wi-Fi 2.4Ghz, and
5Ghz networks, and the iPhone X A1865, which supports 3G,
4G, Wi-Fi 2.4Ghz and 5Ghz technologies. These two iPhone
models were selected from a number of alternatives, both
Android and iOS, because of their logic board architecture,
which is a substrate-like printed circuit board (SLP) architecture. After carefully studying the internal architecture of different smartphone models, we noticed that the lower board of these two smartphone models is mainly populated with RF network components, and there are no RF components on the upper board. We note that this is not a common property for smartphones with this type of SLP architecture. Indeed, for some other models, including other iPhone models, the lower board includes some power-hungry components, such as the camera driver, the touch screen driver, and the back-light driver.

For the two selected models, the smartphone logic board is made of two logic boards soldered together, known as the upper board and the lower board. The upper board is connected to the battery and has the main power management integrated circuit (PMIC), which then distributes power to all logic board components and supplies the lower board with the main power rail. The lower board is where all the RF module parts are grouped together. This includes baseband and all-band power amplifiers, antennas, transceivers, Wi-Fi and Bluetooth modules, the baseband power management unit (BB_PMU), modulators, etc. The BB_PMU generates all the sub-power rails to supply RF module electronic components on the lower board.

Both the upper and lower board PCBs are equipped with components on both sides, designated as faces A and B. On face A of the lower board, copper pads encircle the board along its edge, facilitating connections for signal data lines and power supply lines. These pads serve as the footprint for the face B of the upper board. Once both logic boards are connected and soldered together, solder balls are applied in this area.

The hardware-modification technique we employ involves disconnecting the vias and traces connecting the upper and lower boards, to ensure no power supply line reaches the lower board. To facilitate measurement, we extended the main power rail from the upper board to outside the phone using 0.1-mm copper wire, as illustrated in Figure 3. This extended rail is then connected to a power monitor instrument before being routed back inside the phone and soldered back to the lower board. In order to boot the phone and make it functional, we need to solder back the lower and upper boards and reconnect the data line from the upper to the lower board. We conduct power consumption measurements on the lower board, by interrupting the main power line trace PP_VDD, responsible for delivering power to the lower board and initially sourced from the upper board. PP_VDD functions as a 4.2V power rail, serving as the primary power supply on the logic board.

## B. Private cellular network set-up

Our objective is to collect UE energy consumption measurements in settings as realistic as possible, using commercial wireless networks. However, these commercial cellular and WiFi networks are under the external control of different

<table><caption>TABLE I: Proposed video quality for the five platforms.</caption><thead><tr><th>Quality</th><th>YouTube</th><th>Amazon Prime</th><th>Netflix</th><th>Apple TV+</th><th>Disney</th></tr></thead><tbody><tr><td>Default</td><td>720p</td><td>Good</td><td>Eco</td><td>Auto</td><td>Eco</td></tr><tr><td>Lowest</td><td>360p</td><td>Good</td><td>Eco</td><td>Auto</td><td>Eco</td></tr><tr><td>Middle</td><td>480p</td><td>Better</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Highest</td><td>720p</td><td>Best</td><td>Maximum</td><td>High</td><td>Auto</td></tr></tbody></table>

operators. Using a private cellular network permits network-side trace collection and allows us to modify different RAN parameters. Therefore, we set up a private 4G cellular network testbed for the purpose of this data collection campaign. The testbed uses the srsRAN 4G open-source software [19], which includes several software parts: srsEPC is an LTE core network implementation and srsENB is an LTE eNodeB base station, running on Linux. The srsENB module requires software-defined radio (SDR) hardware to communicate with commercial smartphones. For this, we use Universal Software Radio Peripheral (USRP) from Ettus (the B210 model) as a RF front end. In order to have the same clock for timestamped events, the data acquisition from the power monitor instrument is done on the same Linux computer that runs the srsRAN 4G software.

## C. Experiment Description

With the video streaming market being very competitive, we investigate the UE energy consumption for five commercially well-known video streaming platforms: Apple TV+, Amazon Prime, Disney, Netflix, and YouTube. As a metric of reference, we use the power consumed per minute of video streaming on the different platforms. We conduct measurements over multiple wireless network technologies and under different conditions, as follows:

* Video streaming quality: Every studied platform has a different approach to video quality, but they all propose at least two types of quality for the videos to be delivered, as shown in Table I. We collect measurement data on each platform by exploring the proposed video quality levels. However, regular users rarely modify this parameter in the corresponding application. Therefore, we fix the default platform quality as a reference when we compare the different platforms.

* Measurement sessions: The video selection is made arbitrarily for each session on each platform. With the exception of YouTube, for any given platform, we are able to select the same video for all collection sessions. The duration of a measurement session is between 15 and 30 minutes of continuous video playback. All streamed videos were 2x or 3x longer than the played duration to have a continuous buffering along the measurement session.

* Configuration of wireless networks: Throughout the entire experiment, we use the same mobile operator (a large nationwide operator) with various generations of mobile technologies (3G, 4G, and 5G NSA), which we
<figure><img src="image_5.png" /><figcaption>(a) Original board.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(b) Reconnection of the two board parts.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(c) Final board setup.</figcaption></figure>

<caption>Fig. 3: Modification of a smartphone SLP logic board.</caption>

select manually at the UE level. These technologies are deployed on the following frequency bands: 900 MHz for 3G, 2100 MHz for 4G, and 3500 MHz for 5G NSA. The distance from the UE to the eNodeB was around 100 meters, and we only changed the time of day during the experiments, while always keeping the same location. During the data collection session on our private 4G testbed, we have a 30 cm distance between the smartphone and USRP (eNodeB). Finally, for our Wi-Fi experiment sessions, the distance between the smartphone and the AP is much shorter, around 70 cm. As in the case of the cellular network, we keep this distance constant for all experiments.

* Additional experiment parameters: The official iOS application is used to stream the videos for every platform, with the exception of YouTube those streamed in the Safari browser. During the measurement session, the screen remains on, and the volume is reduced but not muted. There is no interaction with the smartphone during the video streaming session, and the data collection begins as soon as the user opens the streaming platform application. The photo in Figure 4 depicts the experimental measurement setup in operation during a measurement session on an iPhone X.

<figure><img src="image_8.png" /><figcaption>Fig. 4: Measurement setup during a measurement session on iPhone X</figcaption></figure>

## IV. RESULTS

In this section, we discuss and analyze almost 50 hours of UE energy consumption measurement data from five different platforms (Apple TV+, Amazon Prime, YouTube, Netflix, and Disney), using two different mobile devices and multiple wireless network technologies (commercial 3G, 4G, and 5G, private 4G and WiFi). The duration of the collected data for different combinations of these parameters is shown in Table II. In the following, we look at various factors that affect how much energy the RF module uses while streaming: the wireless network technology, the video streaming platform, the video quality, and the mobile device.

### A. Impact of the streaming platform

Probably the most unexpected finding of this study is the significant variation in terms of UE energy consumption among the five studied platforms, as observed in Figure 5.

<figure><img src="image_9.png" /><figcaption>Fig. 5: Distribution of the power consumption per minute for all platforms.</figcaption></figure>

In this figure, we show the distribution of the power consumed by the UE per minute, while using the default video quality, for each of the platforms. These results aggregate measures over multiple wireless technologies. We note a
<table><caption>TABLE II: Duration of collected data per platform and wireless technology.</caption><thead><tr><th>Device\Technology</th><th>Disney</th><th>Apple TV+</th><th>YouTube</th><th>Netflix</th><th>Amazon Prime</th></tr></thead><tbody><tr><td>X 3G</td><td>00:45:42</td><td>00:54:20</td><td>00:49:30</td><td>00:51:40</td><td>00:58:39</td></tr><tr><td>X 4G</td><td>00:51:40</td><td>00:59:44</td><td>00:47:07</td><td>00:51:11</td><td>1:39:32</td></tr><tr><td>X Wi-Fi 2.4GHz</td><td>00:16:40</td><td>00:19:43</td><td>x</td><td>x</td><td>00:15:31</td></tr><tr><td>X Wi-Fi 5GHz</td><td>00:16:11</td><td>00:15:48</td><td>x</td><td>x</td><td>00:15:36</td></tr><tr><td>X 4G SRS</td><td>00:25:26</td><td>00:31:50</td><td>00:25:11</td><td>00:25:21</td><td>00:51:08</td></tr><tr><td>12 Mini 4G</td><td>3:36:06</td><td>2:25:33</td><td>2:19:35</td><td>2:04:16</td><td>4:41:13</td></tr><tr><td>12 Mini 4G SRS</td><td>00:32:03</td><td>00:43:36</td><td>00:15:48</td><td>00:31:30</td><td>00:31:09</td></tr><tr><td>12 Mini 5G</td><td>2:01:36</td><td>2:36:26</td><td>01:17:01</td><td>3:54:46</td><td>01:36:49</td></tr><tr><td>12 Mini Wi-Fi 2.4GHz</td><td>00:30:58</td><td>00:31:13</td><td>00:32:29</td><td>1:03:05</td><td>00:45:39</td></tr><tr><td>12 Mini Wi-Fi 5GHz</td><td>00:31:23</td><td>00:32:21</td><td>00:35:12</td><td>0:46:57</td><td>00:30:41</td></tr><tr><td>Total sessions duration</td><td>9:50:39</td><td>9:47:14</td><td>7:01:57</td><td>10:44:21</td><td>12:06:02</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>Fig. 6: Power consumption patterns for a streaming session.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 7: A short zoom on the power consumption profiles.</figcaption></figure>

significant difference between the different platforms, with the average power consumption for Apple TV+ and Netflix being 2x inferior to the other platforms.

To better understand the reasons for these differences, we extract an arbitrary, but representative energy consumption trace in Figure 6. In this figure, we temporarily align the time series collected on the five platforms, for visual comparison purposes. A zoom on a short period of these traces is presented in Figure 7. The traces shown in these figures are all collected using the iPhone 12 Mini over a 4G network in the 2100 MHz band.

There are notable differences in the power usage trends identified among the various video streaming platforms. Our examination uncovered clear variations in power consumption patterns during streaming sessions across different platforms. Amazon Prime exhibits a unique and consistent consumption pattern, characterized by sustained power usage that fluctuates between 0.4 W and 1 W over the entire streaming duration. Although there are periodic declines in consumption that reach

a lower point, these declines are relatively brief. In contrast, Netflix and Apple TV+ consistently maintain low power consumption levels, often remaining below 0.1 W for extended periods. This indicates the use of better buffering techniques and efficient network connection management, leading to a better use of the UE RF sleep modes.

Disney exhibits the most elevated power consumption peaks, reaching more than 2 W during periods of maximum usage. YouTube demonstrates comparatively lower overall energy consumption than Amazon Prime and Disney, characterized by more frequent and extended intervals of decreased power usage, often reaching a power as low as 0.1 W.

## B. Impact of the wireless technology

We continue exploring our results by analyzing the outcomes of our measurement campaign to ascertain the impact of various network technologies on power usage during video streaming. This investigation examines the effects of different network technologies: commercial 3G (UMTS 900 MHz), 4G (LTE 2100 MHz), and 5G NSA (3500 MHz), a private 4G deployment denoted as 4G SRS, and Wi-Fi at 2.4 GHz and 5 GHz. As in the previous section, this analysis is based on the default video quality used by the five streaming platforms.

<figure><img src="image_12.png" /><figcaption>Fig. 8: Distribution of the power consumption per minute for different wireless technologies</figcaption></figure>
<table><caption>TABLE III: Supplementary 5G NSA energy consumption with respect to WiFi 5GHz.</caption><thead><tr><td>Platform</td><td>5G NSA vs. WiFi 5GHz</td></tr></thead><tbody><tr><td>Amazon Prime</td><td>280%</td></tr><tr><td>Netflix</td><td>604%</td></tr><tr><td>Apple TV+</td><td>337%</td></tr><tr><td>Disney</td><td>587%</td></tr><tr><td>YouTube</td><td>817%</td></tr></tbody></table>

Figure 8 shows the distribution of the power consumption per wireless technology, aggregating the different streaming platforms. The first observation in this figure is that WiFi presents a much lower energy consumption at the UE level than cellular technologies. We recall that the distance between the UE and the WiFi AP is around 70 cm, much lower than the distance to the cellular tower, which is around 100 meters. Most likely, this results in a much better channel quality for the WiFi links. Moreover, although the data traffic is mainly downlink (i.e. the video stream), the UE still needs to transmit control information on the uplink. The shorter-range WiFi links clearly require a much lower transmission power from the UE, contributing to the results in Figure 8. We also note that WiFi in the 5 GHz band, known to present less interference and, hence, better channel quality, is the most energy efficient in our study.

On the cellular network side, 3G and 4G technologies show similar performance. 5G presents the highest energy consumption among the tested technologies. This might be a consequence of the relatively high radio frequency used by the 5G BS (3500 MHz versus 900 MHz and 2100 MHz for 3G and 4G, respectively). We also note that the 5G NSA deployment is considered a transitory phase towards 5G Standalone (SA), and the operators might have put less effort into optimizing it.

The comparison between cellular technologies and WiFi from an environmental perspective clearly requires more thought. At the location where our tests took place, there were over 20 WiFi APs in communication range, while only one cellular BS. This much denser deployment needs to be considered in any detailed environmental impact study. The cellular network also has the advantage of providing user mobility management. However, from the simple perspective of our UE, selecting 5G instead of WiFi for receiving video content results in a much higher energy consumption, regardless of the streaming platform. As shown in Table III, the supplementary cost of 5G with respect to WiFi ranges from 2.8x in the case of Amazon Prime to 6x for Netflix.

The private 4G SRS network constantly shows higher power consumption than commercial 4G in Figure 8, despite a very short distance between the UE and the USRP. We note that this is not a commercial network, built with relatively cheap hardware. Indeed, the emission power of the USRPs, powered by USB from a computer, is much lower than the one of a commercial BS, and it results in a much weaker signal at the UE, despite the reduced distance.

To further understand the relations between wireless technologies and video platforms, in Figure 9 we show the average power consumption per technology for each of the five platforms. We notice that the main trends observed in Figure 8 are still present: WiFi consumes less than cellular for all the platforms, the private 4G SRS network has a constant and relatively high power consumption in all cases, 5G consumes more power than the other cellular technologies. However, some exceptions exist, and they mainly concern Amazon Prime, which presents a behavior different from the other platforms. Indeed, the WiFi, 3G and 4G power consumption for Amazon Prime is distinctively higher than for the other platforms. Amazon Prime is also the only platform for which 5G consumes less than the other cellular technologies.

### C. Impact of different devices

As explained, we conduct our measurements using two different smartphone models: the iPhone 12 Mini, with a screen resolution of 1080 x 2340, and the iPhone X, which has a screen resolution of 2436 x 1125. For both models, we are able to implement the hardware modifications described in Section III. In this section, we investigate whether the smartphone model has any impact on the measured power consumption. For this, in Figure 10, we show the average power consumption recorded with the two different smartphones for four different wireless technologies: WiFi 5GHz, WiFi 2.4GHz, 4G and 4G SRS. We note that this is the intersection of the wireless technologies available on the two models, since the iPhone 12 Mini does not support 3G with the current operator and the iPhone X model available to us is not 5G compatible. These results show the average power consumption value over all the platforms. However, results per platform (not presented here) bring a similar insight.

Practically, the two different smartphones show very similar power consumption. The WiFi module for the iPhone X consumes slightly more power than the one of the iPhone 12 Mini, but the general trend for the different technologies is the same. The 4G results, be it on the commercial network or on the private one, are very close for the two models.

### D. Impact of video streaming quality

This part aims to examine the influence of the various streaming quality options provided by each platform on the power consumption of the RF module. The objective is to analyze the impact of streaming quality algorithms on energy consumption in mobile devices during streaming sessions. This investigation seeks to discover the complex relationship between video quality optimization and energy efficiency in video streaming services. Adaptive streaming algorithms are crucial in influencing the video streaming experience by dynamically modifying video quality according to several parameters.

In order to accomplish this, we analyze the power consumption patterns linked to different video quality levels provided by each platform, all while maintaining as much as possible
<figure><img src="image_13.png" /><figcaption>Fig. 9: Average power consumption per technology for the five different platforms.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 10: Average power consumption per technology for the two different smartphones.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig. 11: Average power consumption per technology for different video streaming qualities.</figcaption></figure>

<table><caption>TABLE IV: Duration of the collected video sessions for different platforms and streaming qualities.</caption><thead><tr><td>Platform</td><td>Streaming quality</td><td>Data duration</td></tr></thead><tbody><tr><td>Amazon</td><td>Optimal</td><td>0:46:34</td></tr><tr><td>Amazon</td><td>Very Good</td><td>0:46:04</td></tr><tr><td>Netflix</td><td>Maximum</td><td>1:02:00</td></tr><tr><td>Apple Tv +</td><td>High</td><td>1:01:34</td></tr><tr><td>Disney</td><td>Auto</td><td>2:53:34</td></tr><tr><td>YouTube</td><td>480p</td><td>0:45:25</td></tr><tr><td>YouTube</td><td>360p</td><td>0:42:39</td></tr></tbody></table>

equal conditions. To maintain consistency in our experimental setup, we use the same smartphone model for all tests, with specifications including a screen resolution of 1080 x 2340 and a pixel density of 476 PPI. Additionally, the tests are conducted over a 4G radio access network.

Every platform uses different approaches to provide video streaming quality, as summarized in Table I. For example, Disney

and Apple TV+ utilize adaptive streaming technologies, specifically DASH and HTTP Live Streaming (HLS), respectively. For Apple TV+, the default setting is denoted "Eco", whereas Disney is by default set to "Auto". Apple TV+ offers a superior choice called "High Quality", while Disney just has the "Auto" extra option. This "Auto" quality setting takes into account both screen resolution and network bandwidth, and maybe other proprietary factors. On the other hand, Amazon Prime offers three preconfigured quality options for streaming, which are described in terms of estimated traffic: 5.8GB per hour for "Optimal", 1.3GB per hour for "Very Good", and 0.6GB per hour for "Good". In contrast, Netflix provides a similar streaming algorithm with two settings: "Eco" and "Max," which are not automatically adjusted on the mobile application. Finally, for YouTube, there are three possible video qualities, given as screen resolutions: 360p, 480p, and 720p. We collect data for all these possible configurations in
<figure><img src="image_16.png" /><figcaption>Fig. 12: Power consumption trace for Amazon Prime with different video qualities.</figcaption></figure>

terms of video quality. The duration of the video sessions for the different platforms and their non-default configurations is given in Table IV. We recall that the durations of the sessions collected for the default configurations were already provided in Table II.

When analyzing the effects of the various streaming qualities supplied by the different video platforms, significant observations arise. Among the three streaming qualities provided by Amazon Prime ("Good", "Very Good", and "Optimal"), there is a notable resemblance in power usage, as shown in Figure 11b. This result is especially surprising considering the data use rates advertised by Amazon Prime. Indeed, there is almost a factor 10x between the data use announced for "Optimal" configuration (5.8GB per hour) and the one announced for "Good" (0.6GB per hour). Such a significant difference in data volume is usually associated with changes in RF network activity, which in turn impacts power consumption. Nevertheless, when it comes to Amazon Prime, there are no noticeable variations in power usage among the three tested video qualities. Given that we did not measure ourselves the data volume during this setup experiment, a possible explanation could be that the advertised data use is not actually respected. However, a similar observation can be made for YouTube in Figure 11e, where the difference between the three possible video qualities does not reflect in significant differences in terms of power consumption.

To better understand this phenomenon, in Figure 12, we show the aligned time series of power consumption for Amazon Prime with different video quality. We notice that the consumption peaks are very well aligned, in period and in amplitude. Once again, this seems to contradict the announced principle of the three different video quality configurations. One other possibility here would be that Amazon Prime uses more bidirectional traffic than the other platforms. Indeed, in video streaming settings, the power consumption is primarily influenced by the dominant downlink data traffic. On the other hand, it is possible that Amazon Prime power consumption is impacted by an important volume of uplink data traffic. Whatever the reason, the power consumption recorded for Amazon Prime is much higher than for the other platforms (at least in this 4G technology set-up), regardless the video quality.

<figure><img src="image_17.png" /><figcaption>Fig. 13: Power consumption trace for Apple TV+ with different video qualities</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. 14: Power consumption trace for Disney with different video qualities</figcaption></figure>

In the case of Apple TV+ (Figure 11a) and Disney (Figure 11c), the differences between the two possible video quality configurations are small, but follow the expected logic. The "Auto" mode for Apple TV+ consumes less power than the "High" mode, and the Disney "Eco" mode improves on the "Auto" mode. When examining the power consumption for Apple TV+ "Auto" (Figure 13) and Disney "Auto" (Figure 14), we observe parallels in their power usage patterns. One explanation for this consistent behavior across both platforms could be that they use an automated system to optimize streaming performance by taking network bandwidth factors into account. Therefore, this could clarify why Disney "Eco", which probably functions within equivalent principles, likewise demonstrates similar levels of power use.

Netflix displays a clear contrast, with considerable differences between the two streaming options supplied: "Max" and

<figure><img src="image_19.png" /><figcaption>Fig. 15: Power consumption trace for Netflix with different video qualities.</figcaption></figure>
"Eco" quality. The difference in power consumption indicates that the streaming quality settings of Netflix have a significant effect on power usage, with "Eco" requiring significantly less power than "Max". According to our studies, Netflix "Eco" uses about 25% of the power that Netflix "Max" does. This demonstrates the significant energy efficiency improvements that may be achieved by using network resources more wisely, as it is also illustrated in the traces presented in Figure 15. The power consumption peaks for "Eco" are much lower, compensated by what appears to be a longer buffering phase at the beginning of the trace. These results seem to indicate that the video buffering strategy and the use of adaptive streaming algorithms, as used by Netflix, Apple TV+, and Disney, have a more significant impact on the power consumption than the sheer reduction in data size, the strategy chosen by Amazon Prime and YouTube.

## V. CONCLUSION

Our study provided a detailed examination of UE energy consumption across various video streaming platforms using two different smartphones, the iPhone X and iPhone 12 Mini. By employing hardware-level power measurement techniques, we achieved precise and granular data regarding the energy consumption of wireless network modules during a video streaming service. Our research spanned multiple RAN technologies, including Wi-Fi (2.4 GHz and 5 GHz), 3G (UMTS 900 MHz), 4G (LTE 2100 MHz), and 5G NSA (3500 MHz), tested across both commercial networks and a private cellular network setup.

The results of our study revealed significant variations in power consumption patterns among different video streaming platforms, which were more important than network technologies used or the quality of the video streamed. The comprehensive nature of our study, which included almost 50 hours of streaming sessions, highlights the critical role of application behavior in influencing UE power consumption. These findings offer valuable insights for researchers and developers, guiding them toward optimizing power usage on mobile devices and improving energy efficiency in video streaming services.

## ACKNOWLEDGMENTS

This work was partially funded by the CHIST-ERA ECOMOME project.

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