with the skew parameter $\alpha=0.7$, i.e., the probability of an incoming
request for the $i^{th}$ channel in each peer group is given as
$prob(i)=\frac{1/i^{\alpha}}{\sum_{j=1}^{K}1/j^{\alpha}}$, where $K=5$.

We use Docker image jrottenberg/ffmpeg FFmpeg (2020) to measure the segment
transcoding time on the VTS. Moreover, we execute the transcoding function via
FFmpegKit FFmpegKit (2020) on an iPhone 11 (Apple A13 Bionic, iOS 15.3), a
Xiaomi Mi11 (Snapdragon 888, Android 11), and a PC (Apple M1, MacOS 12.0.1) to
measure the transcoding time on the heterogeneous P2P network. Power
consumption is measured by device tools, e.g., Android Energy Profiler and
Android Battery Manager. Moreover, we utilize the CodeCarbon Schmidt et al.
(2021) project to measure power consumption for the client PCs. In the
literature, the bandwidth value from an edge server to CDN servers is assumed
higher than to the origin server Yi et al. (2017); Al-Abbasi et al. (2019).
Therefore, the Linux Wondershaper tool WonderShaper (2020) is employed to set
50 and 100 Mbps as a bottleneck bandwidth in different paths from the CDN and
origin servers to the VTS, respectively.

To emulate the mobile network conditions, we assume 250 peers start the
experiments initially, and then every three seconds a new peer joins the
sessions. Furthermore, we use a real 4G network trace Raca et al. (2018)
collected on bus rides for links between peers to edge servers in all
experiments. The average bandwidth of this trace is approximately 3780 kbps
with a standard deviation of 3190 kbps. Note that the VTS directs the first
peer to the best CDN server (in terms of lowest latency), while other
participating peers can be connected on both CDN and P2P links. In hybrid
P2P-CDN systems like ALIVE, most costs are related to backhaul bandwidth and
edge computational costs. Therefore, other service costs are negligible
compared to the aforementioned costs. The computational and bandwidth costs
(i.e., $\Delta_{co}$ and $\Delta_{bw}$) are set to $0.029\$$ per CPU per hour
and $0.12\$$ per GB, respectively Amazon (2020b). The weighting parameter
$\beta$ and the monitoring interval are set to 0.5 and one second in all
experiments, respectively.

#####  6.2.3.3 Evaluation Methods, Scenarios, and Metrics

Methods for Comparison: In the ALIVE performance evaluation, we first
investigate the SR models’ behavior. Next, we analyze the performance of the
proposed optimization model and heuristic algorithms. Finally, we refer to our
proposed heuristic methods and present practical testbed results to evaluate
ALIVE’s effectiveness compared to the following baseline schemes.

  1. 1.

Non Hybrid (NOH): The NOH system is regular CDN-based streaming with no P2P
support.

  2. 2.

Simple Edge-enabled Hybrid (SEH): The SEH system employs a simple VTS server
without caching and transcoding capabilities. In this system, peers only can
be served via one of the actions 1, 7, or 8 (Fig. 6.6).

  3. 3.

Non Transcoding-enabled Hybrid (NTH): Like in most works, there is no
transcoding capability in this approach. In an NTH-based system, peers only
can be served through one of the actions 1, 4, 7, or 8 (Fig. 6.6).

  4. 4.

Edge Caching/Transcoding Hybrid (ECT): ECT does not include transcoding at the
peer side, and requests can be served via all actions except actions 2 and 3.

  5. 5.

Non SR-enabled Hybrid (NSH): In the NSH-based system, there is no SR feature
on the peer side and requests can be served via all actions except action 2.

Note that we use our testbed with similar settings and topology in all systems
for fair and robust comparisons. Moreover, all aforementioned baselines
execute a simplified version of the proposed GBA algorithm to answer the
questions mentioned in Section 6.2.1.1. We use the method discussed in Section
4.1.3 to calculate the results reported in the next section.

Description of Scenarios: We compare the performance of the ALIVE methods with
the aforementioned baseline approaches in four scenarios.

  1. 1.

Scenario I: This scenario studies the impact of changing SR models on the
perceptual quality metrics. Moreover, it investigates the latency, quality
distortion, and energy overheads of running computational-intensive tasks,
i.e., transcoding and super-resolution, on peers.

  2. 2.

Scenario II: In this scenario, we provide a performance comparison between the
proposed optimization model and the heuristic algorithm by conducting several
experiments. These experiments study the impact of changing key parameters
like the bitrate ladder size, the number of peers’ requests, and live channels
on the optimization model and heuristic algorithms performance metrics.

  3. 3.

Scenario III: In this scenario, we run a series of experiments on the designed
testbed to assess the performance of the explained systems in terms of common
QoE-related parameters compared to other approaches.

  4. 4.

Scenario IV: As a final scenario, the behavior of different frameworks in
terms of network utilization/cost metrics will be analyzed on the testbed.

Evaluation Metrics: Perceptual quality, algorithm performance, QoE-related,
network utilization/cost metrics are defined as follows:

  1. 1.

Perceptual quality metrics:

     * •

VMAF: Video Multi-method Assessment Fusion Li et al. (2016) is a learning-
based objective quality metric (with a range from 0 to 100) that predicts the
perceived video quality.

  2. 2.

Algorithm performance metrics:

     * •

ETV: Execution Time Values (in seconds) for the different ALIVE schemes.

     * •

NOV: Normalized Objective Values (i.e., of the objective function Eq. 6.26)
for the ALIVE schemes (in %).

  3. 3.

Common QoE metrics:

     * •

ASB: Average Segment Bitrate (in Mbps) of all downloaded segments.

     * •

AQS: Average Quality Switches, i.e., the number of segments whose bitrate
levels change compared to the previous ones.

     * •

ASD: Average Stall Duration, i.e., the average of total video freeze times (in
seconds) in all clients.

     * •

ANS: Average Number of Stalls, i.e., the average number of rebuffering events.

     * •

ASL: Average Serving Latency (in seconds) for serving all clients, including
transmission latency plus computational latency.

     * •

APQ: Average Perceived QoE (with a range from 0 to 5), calculated by ITU-T
Rec. P.1203 mode 0 P.1203 (2020).

  4. 4.

Network utilization/cost metrics:

     * •

CHR: Cache Hit Ratio (in %), defined as the fraction of segments fetched from
the CDN, VTS, or peers.

     * •

ETR: Edge Transcoding Ratio (in %), i.e., the fraction of segments transcoded
at the VTS.

     * •

PTSR: Peer SR and TR Ratio (in %), i.e., the fraction of segments constructed
by TR or SR at the peers.

     * •

BTL: Backhaul Traffic Load (in Gbps), i.e., the volume of segments downloaded
from the origin server.

     * •

EEC: Edge Energy Consumption (in kWh) for running ETR.

     * •

NCV: Network Cost Values (in $), including computational and bandwidth costs.

We discuss details of the scenarios and ALIVE performance evaluation in the
next section, where each experiment is executed 20 times to ensure accuracy,
and the average values and standard deviations are reported in the
experimental results.

#####  6.2.3.4 Evaluation Results

Scenario I: Running transcoding or super-resolution on peers (i.e., PTR or
PSR) must be sufficiently fast, not significantly imposing an extra delay to
the ALIVE system, and not considerably consuming peers’ battery; otherwise,
the clients’ requests may use other actions that make the network and edge
server congested. Hence, in the first scenario, we conduct experiments to
investigate the latency, quality distortion, and energy overheads of running
transcoding and super-resolution tasks on peers.

The first experiment of this scenario measures the client’s power consumption
when the client utilizes the PSR function to jump to a higher representation
level in the bitrate ladder from the lower received representation (Fig
6.12(a)). It is noteworthy that ALIVE only operates the SR on the targeted
client as the last step of playing out the video. This is because the output
of the SR model is a decoded representation, and then ALIVE directly displays
it. Therefore, the SR process is accomplished without inducing any extra
delay, except the SR processing delay, for preparing the requested
representation. However, applying SR at the edge or other network layers
requires a re-encoding phase before transmitting it to the client,
consequently imposing an extra encoding delay on the system. The results of
this experiment are obtained for two upscaling operations on the client PC
peers by the discussed CARN-M SR model. The used representations in this
experiments are (89 kbps - 240p) $\rightarrow$ (262 kbps - 360p) and (262 kbps
- 360p) $\rightarrow$ (791 kbps - 720p). The power consumption of peers, when
they (i) play the low representation video, (ii) transcode the video from a
higher representation, or (iii) apply SR from a low resolution, is plotted in
Fig. 6.12(a). As obvious from the results, the SR application consumes much
more battery than playing out the lower representation directly (about 52%),
while this rise is about 38% when transcoding the requested representation
from the highest representation (i.e., (4.2 Mbps - 1080p) $\rightarrow$ (262
kbps - 360p) or (791 kbps - 720p)).

Fig. 6.12: Performance comparison of applying peer’s SR on a lower
representation in terms of (a) power consumption, (b) VMAF score, (c)
VMAF/Bitrate, and (d) bandwidth saving.

We run further experiments to calculate the power consumption (i.e., power
values are given as kWh$\times 10^{-3}$ and percentage of battery usage) for a
five-minute video (i.e., 150 segments) when different actions are executed
simultaneously on one peer (e.g., play video I, transcode video II, upscaled
video III, simultaneously). As reported in Table 6.4, the SR operation
continues to use the most power, followed by the transcoding (TR) function,
while battery consumption of playing back a video (i.e., PLY) is not
comparable with them. Moreover, a combination of PLY, TR, and SR tasks does
not put a significant burden on the peers’ batteries (i.e., 4.04% and 7.74%)
compared to the energy used to play, transcode, or upscale a video
individually. Note that the reported values for each segment with two seconds
durations are negligible (i.e.,
$\frac{measured~{}Power~{}or~{}Battery}{segment~{}numbers}$).

Table 6.4: Power consumption and battery usage of different approaches for a
5-min. video on peers.

Method | $240p\rightarrow 360p$ | $360p\rightarrow 720p$
---|---|---
Power (kWh$\times 10^{-3}$) | Battery | Power (kWh$\times 10^{-3}$) | Battery
PLY | 119 | 0.39% | 152 | 0.49%
TR | 130 | 0.42% | 352 | 1.14%
SR | 955 | 3.09% | 1,862 | 6.03%
TR + PLY | 237 | 0.77% | 479 | 1.55%
SR + PLY | 1,020 | 3.30% | 1,915 | 6.20%
SR + TR | 1,043 | 3.71% | 2,036 | 7.25%
SR + TR + PLY | 1,084 | 4.04% | 2,079 | 7.74%

The second experiment examines the quality of the video upscaled by SR in
terms of the VMAF metric. As illustrated in Fig. 6.12(b), by applying the
peer’s SR, we significantly increase the VMAF score of the displayed video
compared to the lower representation (by at least 41%). Although the requested
target representation still outperforms the VMAF score obtained by the SR
(Fig. 6.12(b)), applying SR provides the best VMAF score per kbps of data
delivered to the client (Fig. 6.12(c)). We repeat the first and second
experiments for different representations in the bitrate ladder to find a
sweet spot in the trade-off between quality improvement and power consumption.
As depicted in Fig’s. 6.12 (a) and (b), we discover that the sweet spot is to
go up one step in the bitrate ladder, as increasing resolution too much would
result in significant distortion. Therefore, upscaling the targeted
representation from the closest one would result in a pleasant user’s QoE (in
terms of the VMAF score) and less power consumption.

In the third experiment, we investigate a solution to discover the exact
bandwidth saving that can be achieved by applying SR. Imagine we use the
$Rep_{1}=$ (89 kbps - 240p) representation to build $Rep_{2}=$ (262 kbps -
360p) by SR. We first apply the SR on $Rep_{1}$ to increase the resolution to
$Rep_{2}$. We then calculate the VMAF score of $Rep_{2}$. In the next step, we
encode the input source video with a requested resolution (i.e., 360p) in
different bitrates (starting from the lower bitrate, i.e., 89 kbps, and
gradually increasing it) to find the bitrate in which the VMAF score matches
$Rep_{2}$. We have applied the same approach to all representations in the
bitrate ladder and found that applying SR can save around $24.53\%$ bandwidth
on average while maintaining the same visual quality (Fig. 6.12(d)).

Table 6.5: Transcoding time and VMAF scores for 3-min. video on different peer types. Input BR | Target BR | Client PC | Client Mobile
---|---|---|---
Time (s) | VMAF | Time (s) | VMAF
4219k | 89k | 4.31 | 15.38 | 15.98 | 13.75
4219k | 262k | 5.33 | 44.61 | 18.32 | 42.13
4219k | 791k | 11.74 | 76.21 | 39.28 | 73.14
4219k | 2484k | 20.44 | 93.33 | 74.91 | 91.53
2484k | 89k | 3.80 | 14.35 | 16.55 | 13.01
2484k | 262k | 4.83 | 42.27 | 18.82 | 40.02
2484k | 791k | 11.36 | 71.56 | 39.76 | 69.06
791k | 89k | 2.05 | 12.21 | 10.43 | 11.24
791k | 262k | 3.35 | 36.33 | 14.81 | 34.76
262k | 89k | 1.28 | 11.01 | 5.85 | 10.32

In the final experiment of this scenario, we measure the peers’ transcoding
times and VMAF scores for a 3-min video. We note that the VMAF score
differences in mobile and PC peers are due to using different transcoding
presets, i.e., veryfast and fast, respectively. It is worth mentioning that
transcoding needs decoding a video into raw frames and then re-encoding those
frames into new frames. Thus, transcoding time at the peers is equal to the
encoding time due to leveraging the video processing that is already being
done to capture or view video. Therefore, peer transcoding by the PTR function
can already shorten one decoding time. As shown in Table 6.5, running
transcoding for the whole video takes 1.28–20.44 seconds (0.014–0.22 seconds
per segment) and 5.85–74.91 seconds (0.065–0.8 seconds per segment) on PC and
mobile peers, respectively. Moreover, the results in the VMAF column indicate
that using the closest input bitrate to the target bitrate achieves better
VMAF scores.

Scenario II:  In the second scenario, we compare the performance of the
proposed MILP optimization model with the GBA-based heuristic method in terms
of the algorithm performance metrics (Fig. 6.13).

In the first experiment of this scenario, we measure the ETV metric by (i)
increasing the number of peers’ requests ($Req\\#$), (ii) increasing the
number of live channels ($Ch\\#$), and (iii) increasing the number of
representations in the bitrate ladder ($|ladder|$) to assess ALIVE’s GBA
scalability and practicality. Fig. 6.13(a) highlights the ETV comparison of
the ALIVE GBA scheme with the MILP model scheme when the VTS must handle
different numbers of simultaneous requests (i.e., $Req\\#$ = 100, 500, 1000,
and 5000) received from peers. It should be noted that all experimental
settings, including the number of other components, are the same as discussed
in Section 5.2.3.1 for this experiment. The results show that, by increasing
$Req\\#$, the ETV value of the MILP model increases exponentially, while the
proposed lightweight GBA achieves much less ETV.

Fig. 6.13: Impact of (a) increasing the number of arriving requests
($Req\\#$), (b) increasing the number of live channels ($Ch\\#$), and (c)
changing the bitrate ladder, on the performance of the MILP model and the
proposed heuristic method in terms of ETV and (d) NOV.

In the second and fourth experiments, we investigate the impact of increasing
the number of live channels (i.e., $Ch\\#$) and the bitrate ladder on the
values of ETV, respectively. As depicted in Fig. 6.13(b), increasing the
number of live channels (i.e., $Ch\\#$ = 5, 10, 20) does not have a
significant effect on the ETV values and slightly increases it in both the
MILP and heuristic methods. In the next experiment, we examine the behavior of
the proposed schemes when using a different number of representations in the
bitrate ladders (i.e., $|ladder|$ = 5, 8, 12). As plotted in Fig. 6.13(c),
replacing a bitrate ladder with a ladder with more representations increases
the ETV values noticeably. In fact, having a bitrate ladder with more
representations boosts the problem search space, consequently causing schemes
to spend much more time deciding on the best nodes and actions. It is evident
in all experiments that the proposed GBA-based heuristic scheme outperforms
the MILP model significantly in terms of the ETV, showing minimal growth with
changing the above-mentioned parameters (see Fig’s. 6.13(a–c)).

In the last experiment of this scenario, we measure the normalized objective
function value (i.e., NOV) metric by varying the number of requests
($Req\\#$). As displayed in Fig. 6.13(d), the MILP model surpasses the GBA
method regarding the NOV value for all $Req\\#$. We note that having more live
channels and/or replacing the bitrate ladder result in similar trends for the
NOV. As described earlier, the MILP model is executed for all peers’ requests,
while the GBA method is run based on regions’ queues in a time-slotted
fashion. The results in this experiment confirm that the GBA-based heuristic
solution is a sub-optimal solution, but as shown in Fig. 6.13(d), they are
close to the MILP model.

Scenario III:  This scenario investigates the performance of the proposed
ALIVE GBA method on the introduced testbed and compares the common QoE metrics
with baseline methods. This scenario does not use the ALIVE MILP method due to
its high time complexity and impracticality in large-scale environments.

As illustrated in Fig. 6.14(a), ALIVE serves HAS clients with higher ASB in
both BOLA and SQUAD-based systems. This is because ALIVE uses all
computational, caching, and bandwidth resources in a hybrid P2P-CDN network.
Similar to ASB, Fig. 6.14(b) demonstrates that ALIVE decreases the AQS for
both ABR algorithms by at least 34.5%. This is again due to using all provided
resources in the ALIVE system for fetching, running TR, or operating SR
compared to other methods.

Fig. 6.14: Performance of the ALIVE system compared with other baseline
methods in terms of (a) ASB, (b) AQS, (c) ANS, (d) ASD, (e) ASL, and (e) APQ
for 350 clients running BOLA and SQUAD ABR algorithms.

The effectiveness of different systems in terms of ANS and ASD, as essential
factors of users’ QoE, are shown in Fig’s. 6.14(c) and (d), respectively. It
is worth noting that buffer-based ABR algorithms (e.g., BOLA) are focused on
the buffer status for adaptation decisions; thus, they work better than SQUAD-
based players in terms of the ANS and ASD metrics. Utilizing the GBA algorithm
as a decision-maker for selecting the best node and best action with minimum
delay, enables the ALIVE system to serve HAS requests with fewer ANS and ASD
values. To be precise, ALIVE decreases ANS by at least 40% and shortens ASD
for BOLA by at least 43% compared to other systems. Results for the SQUAD-
based systems follow the same trend, where ANS and ASD values obtained by the
ALIVE system outperform baseline approaches by at least 42% and 45%,
respectively. Moreover, we measure the ASL, which is considered an essential
part of the end-to-end delay. As illustrated in Fig. 6.14(e), the ALIVE system
demonstrates superior performance for both ABR algorithms in terms of the ASL
metric. In fact, downloading requested segments via the most appropriate
actions with minimum serving time (computation plus transmission) and from the
best node with high available resources (i.e., bandwidth, computation)
significantly shortens both ASD and ASL values, where this improvement for ASL
is at least 40% compared to other methods. It is noteworthy that since SQUAD-
based players download higher-quality segments and their ASD values are longer
than those of BOLA-based players (Fig.6.14(a) and (d)), their ASL values are
higher than those of BOLA-based players.

Although enhancing each QoE parameter individually (e.g., ASB, AQS, ANS, ASD,
and ASL) can generally improve the users’ satisfaction, having a comprehensive
standard model is essential to analyze the system behavior in terms of users’
QoE improvement. Therefore, we use a standard QoE model P.1203 (2020) to
evaluate ALIVE’s performance in terms of the APQ metric. As plotted in Fig.
6.14(f), the value of the APQ metric is between zero and five, where zero is
the worst-perceived quality and five is the best-perceived quality. It is
worth mentioning that stall duration (measured by the ASD metric)
significantly affects APQ compared to other common QoE parameters. Hence,
BOLA-based players with better ASD values achieve better APQ compared to
SQUAD-based players. As shown in Fig. 6.14(f), the ALIVE system presents
superior performance for both ABR algorithms. In fact, ALIVE enhances APQ by
at least 22% compared to other baseline techniques. Improving the ASB, AQS,
ANS, and particularly shortening the ASD values, is the main reason for
outperforming the NSH system as the second-best scheme in terms of the APQ
metric.

Scenario IV: In the fourth scenario, we analyze the performance of the
proposed ALIVE GBA scheme on the testbed regarding the network
utilization/cost utilization metrics and compare the obtained results with
baseline methods. Similar to scenario III, we do not utilize the ALIVE MILP
model in this scenario.

The performance analysis of the ALIVE scheme concerning the CHR and ETR
metrics are depicted in Fig’s. 6.15(a) and (b), respectively. It should be
noted that different frameworks encounter cache miss events under various
conditions, which are listed as follows:

  * •

Condition I: The requested representations are not held in CDN servers (e.g.,
for NOH).

  * •

Condition II: The requested representations do not exist in CDN servers or
peers (e.g., for SEH).

  * •

Condition III: The requested representations are not in CDN servers, VTS, or
neighboring peers (e.g., for NTH).

  * •

Condition IV: Available bandwidth is insufficient to fetch the requested
representation from the selected server (i.e., CDN, or VTS) or peer (e.g., for
NOH, SEH, or NTH).

  * •

Condition V: The requested or higher representations do not exist in CDN
servers; the VTS server does not hold the requested or higher representations;
the VTS’s resources are insufficient for serving requests via the ETR
function. In addition, the requested representations are not cached in any
adjacent peer. (e.g., for ECT)

  * •

Condition VI: Peers do not have the candidate representations (i.e., higher or
lower) for serving requests through the PTR or PSR functions; peers’ resources
(i.e., computational or bandwidth) are inadequate for serving requests by the
PTR or PSR operations (e.g., for NSH and ALIVE).

The CHR metric plotted in Fig. 6.15(a) shows that ALIVE outperforms the NOH,
SEH, NTH, and ECT approaches by at least 12% due to its ability to fetch
requested, higher, or lower representations in a hybrid system and then to
reconstruct the requested representations via PRT, PSR, or ETR functions.
Moreover, ALIVE slightly improves over the NSH system regarding the CHR
slightly (by 4%) due to having an extra action 3 for fetching the lower
representation from adjacent peers. As shown in Fig. 6.15(b), ALIVE improves
the ETR metric of the ECT system by 53% due to using idle computational
resources of the P2P network for running the PTR and PSR functions. Note that
since NOH, SEH, and NTH are not transcoding-enabled systems, the ETR metrics
does not apply. Moreover, since ALIVE employs the PSR function besides PTR, it
improves over the NSH system by 32% in terms of ETR.

Fig. 6.15: Performance of the ALIVE system compared with other baseline
methods in terms of (a) CHR, (b) ETR, (c) PTSR, (d) BTL, (e) EEC, and (e) NCV
for 350 clients running BOLA and SQUAD ABR algorithms.

The PTSR metric in Fig. 6.15(c) indicates that ALIVE increases the usage of
available computational resources of the P2P network (by 36%) compared to the
NSH framework, which is the only baseline approach that employs peers’
computations for running PTR. This is because ALIVE operates super-resolution
by the PSR function besides direct fetching and transcoding for serving
requests. However, improving the cache hit ratio (i.e., CHR) and constructing
requested representations at the edge or P2P network (i.e., by ETR, PTR, or
PSR) directly affects the BTL metric since the system downloads fewer segments
from the origin server; ALIVE thus outperforms other systems and significantly
reduces fetching representations from the origin server, by at least 24%,
compared to other frameworks (Fig. 6.15(d)).

The next experiment analyzes the EEC metric to assess edge server energy
consumption for producing requested representations in different systems. As
is evident from Fig. 6.15(e), ALIVE has better EEC results than the NSH and
ECT methods (the only transcoding-enabled baseline systems) and reduces energy
consumption by at least 31%. Indeed, serving requested representations through
(i) the representations cached in all layers of the network (CDN, P2P, and
edge), (ii) the representations transcoded by transcoding-enabled peers, or
(iii) the resolutions up-scaled from the lower representations with smaller
sizes at the peer, allows ALIVE to transcode fewer segments at the edge,
consequently consuming less energy. In the final experiment, we measure the
incurred costs to providers, based on network bandwidth and computational
costs, in different systems. As plotted in Fig. 6.15(f), the ALIVE scheme
achieves the best performance among all the methods and reduces network costs
by at least 34%. Employing all resources provided by peers improves the CHR
and ETR metrics and decreases BTL as a network cost factor.

###  6.3 Summary

In this chapter, we utilized the benefits provided by the CDN and P2P delivery
networks to introduce a hybrid P2P-CDN NAVS system for HAS clients in live
streaming applications, named RICHTER. We leveraged the NFV and edge computing
paradigms to introduce a new in-network component with a broader and central
view of the network called Virtualized Tracker Server (VTS), placed at the
edge of hybrid P2P-CDN networks as follows. RICHTER utilized all peers’
potential idle computational, caching, and bandwidth resources to serve HAS
clients by transcoding/fetching from peers, by transcoding/fetching from VTS,
or by fetching from CDN/origin. Besides considering resource limitations, we
designed an Action Tree, including all possible actions for serving clients’
requests employed by VTSs at the edge of a P2P-CDN network. Taking into
account information provided by peers (i.e., cache occupancy, available
bandwidth values, and available computation), by CDNs (i.e., cache server
occupancy, available bandwidth values), and by video players (i.e., requested
video quality levels), RICHTER’s VTSs run an MILP optimization model to
determine suitable nodes for serving requests. Due to the NP-completeness of
the proposed MILP model, we extended the VTSs by designing an OL-based
approach that uses an unsupervised Self-Organizing Map (SOM) technique for
action selection decisions. To test the practical deployment of the proposed
solution, we deployed RICHTER and analyzed its performance through experiments
conducted in a large-scale testbed including 350 clients and compared its
results with selected baseline approaches. The experimental results
demonstrated that RICHTER achieved higher users’ QoE (by at least 59%), lower
latency (by at least 39%), and better network utilization (by at least 70%)
compared to other methods.

We also proposed a latency- and cost-aware hybrid P2P-CDN system for live
streaming clients, called ALIVE. In addition to using video transcoding, ALIVE
enables video super-resolution over the P2P network to reconstruct the
requested representations from the available lower quality/bitrate
representation. Considering this new feature, ALIVE redesigned RICHTER’s
Action Tree to cover all feasible resources (i.e., storage, computation, and
bandwidth) provided by peers, edge, and CDN servers for serving peer requests
with acceptable latency and quality. Moreover, we formalized the problem as a
multi-objective MILP optimization model, including both clients’ latencies and
delivery costs. To alleviate the optimization model’s high time complexity, we
proposed a lightweight greedy-based heuristic algorithm as well. Experimental
results of comprehensive scenarios over a large-scale cloud-based testbed
displayed that ALIVE enhanced the users’ QoE (by at least 22%), decreased
delivery cost (by at least 34%), shortened clients’ serving latency (by at
least 40%), improved edge server energy consumption (by at least 31%), and
reduced backhaul bandwidth usage (by at least 24%) compared to baseline
approaches.

##  Chapter 7 Conclusions and Future Work

In the following, we highlight the contributions of the thesis and several
directions for future work.

###  7.1 Conclusions

Over the past decade, VoD and live video content started to dominate Internet
traffic. This trend has continued since the number of powerful end devices
capable of requesting streams with higher qualities and resolutions has
increased. However, this has also led to rising network costs and resource
requirements; thus, today’s delivery of OTT services is challenging for
Internet and content providers. Therefore, new approaches with more intense
network contributions, either by improving the performance of the existing
delivery networks (i.e., CDNs) or by involving the user devices as active
contributors in the content delivery process, are needed to satisfy clients
with acceptable quality and latency values. This thesis aimed to design,
develop, deploy, and evaluate fully transparent Network-Assisted Video
Streaming (NAVS) frameworks for HTTP Adaptive Video Streaming (HAS) clients by
utilizing new networking paradigms. We presented seven contributions that
addressed the common design goals of NAVS frameworks for HAS clients. The
design and evaluation of our proposed mechanisms were guided by the challenges
and research questions (RQs) identified in Chapter 1. In the following, we
summarize the contributions of the designed frameworks based on the RQs
addressed. Note that the italic keywords denote the primary constituent of our
contributions for addressing RQs.

  1. RQ1

How can SDNs/CDNs provide assistance for HAS clients in order to improve media
delivery services?
To answer this RQ, we introduced five NAVS frameworks (see Fig. 1.5 to find
their differences and relations), each equipping MEC servers with multimedia
VNFs (e.g., Virtual Proxy Function, Virtual Transcoding Function, Virtual
Cache Function) and enabling them to communicate and collaborate with the SDN
controller, consequently serving HAS clients’ requests with better QoE and
latency. The principal contributions of the proposed frameworks are as
follows:

     * •

Design of new server/segment selection policies: In Chapter 3, we introduced
the ES-HAS (Section 3.1) and CSDN (Section 3.2) frameworks that utilized edge
servers with the Virtual Reverse Proxy function to act as a gateway between
HAS clients and the network and gather their information with support from the
SDN controller. These edge servers also were enabled to aggregate clients’
requests and send only one request in order to prevent sending identical
requests to other servers issued by multiple clients. We introduced a
server/segment selection policy method, which enables ES-HAS to serve HAS
requests in three ways: (i) by the originally requested quality level from a
cache server with the shortest fetch time, (ii) by overwriting the original
request through a replacement quality (only better quality levels with minimum
deviation) from a cache server, or (iii) by the originally requested quality
level from the origin server. CSDN expanded the ES-HAS system by incorporating
the Virtual Transcoding Function into the edge server, plus extending the
server/segment policy to decide whether to transcode to the requested quality
from a higher quality level available at the edge in case that quality was not
present in the cache servers.

     * •

Design of VNFs that can be chained under an SDN controller’s coordination to
establish different types of video streaming services: In Chapter 4, we
introduced SARENA (Section 4.1), which employed three multimedia VNFs: VPF,
VTF, and VCF. We designed chains of these VNFs to serve various multimedia
service requests, e.g., VoD and live video streaming, (i) by using VCF to
fetch them from the edge, (ii) by using VTF to transcode them from higher
quality levels fetched from a CDN server, (iii) by using VTF to transcode them
from higher quality levels fetched from the edge (i.e., by VCF), (iv) fetching
from a CDN, or (v) fetching from the origin. Moreover, we introduced request
scheduler and edge configurator modules on the SDN controller to apply
appropriate scheduling decisions for various multimedia requests and scale
edge server resources based on service requirements dynamically, respectively.
In addition, a lightweight heuristic algorithm that can be run on the edge
servers was proposed to mitigate the time complexity of the proposed requests
scheduling policy.

     * •

Collaboration between edge servers under an SDN controller’s coordination: In
Chapter 5, we presented two collaborative edge-assisted frameworks for HAS
clients, namely LEADER (Section 5.1) and ARARAT (Section 5.2). These
frameworks utilized the capabilities of the SDN controller to establish
collaboration between each Local Edge Server (LES) that receives HAS requests
and its Neighboring Edge Servers (NESs). These frameworks introduced a set of
actions called Action Tree to serve client requests with acceptable latency
and quality, considering all available resources (i.e., storage, computation,
and bandwidth provided by edge, CDN, and origin servers). Fetch the requested
quality from the LES or an NES edge server that holds that and has the highest
available bandwidth or transcode the requested quality in the LES or an NES
that has the highest available computational resources are examples of the
actions defined in the Action Tree. Furthermore, the SDN controller in both
systems employed a bandwidth allocation module that assists edge servers in
achieving appropriate bandwidth resources to serve their requests from the
most suitable servers in terms of time. ARARAT extended LEADER’s Action Tree,
considered network cost in the action decision-making process, and proposed
extra coarse-grained and fine-grained heuristic algorithms to solve the
problem.

  2. RQ2

How can resources (i.e., computation, storage, bandwidth) provided by the HAS
clients be utilized to enhance media delivery services?
In order to answer this RQ, in Chapter 6, we utilized the benefits provided by
the CDN and P2P delivery networks to introduce two hybrid P2P-CDN NAVS systems
for HAS clients in live streaming applications, which are latency-sensitive
services. These systems are called RICHTER (Section 6.1) and ARARAT (Section
6.2). We leveraged the NFV and edge computing paradigms to introduce a new in-
network component with a broader view of the network, namely Virtualized
Tracker Server (VTS), placed at the edge of these hybrid P2P-CDN systems.
These frameworks employed idle computational resources and available bandwidth
of HAS clients (i.e., peers) to offer distributed video processing services,
such as video transcoding and video super-resolution. We also considered all
feasible resources (i.e., storage, computation, and bandwidth), provided by
peers, edge, and CDN servers and proposed Action Trees, including all possible
actions to serve requests with the most appropriate action. Using the P2P
network and transmitting the requested quality representation directly from
the most suitable adjacent peer, transcoding the requested quality
representation from a higher quality at the peer, upscaling to the requested
resolution by the super-resolution function from a lower resolution received
from the adjacent peer in the P2P network, or transcoding to the requested
quality representation from a higher quality at the VTS and then transmitting
it from the VTS to the peer are example actions of these Action Trees.
Moreover, we proposed heuristic methods (based on online learning or
lightweight algorithms) that are designed to play decision-maker roles in
large-scale practical scenarios.

  3. RQ3

What is the utility of the proposed assistance and collaboration service?
To answer this RQ, a broad spectrum of evaluation metrics was used as the
principal utilities of our proposed NAVS frameworks (Chapters 3–6). These
metrics can be categorized as follows:

     * •

User QoE metrics (application QoS): quality bitrate, number of quality
switches, number of stalls, stalling duration, serving times, VMAF Li et al.
(2016) values, standardized perceived quality P.1203 (2020).

     * •

Network utilization metrics: cache hit ratio, transcoding ratio at the edge,
transcoding ratio at the P2P network, super-resolution ratio at the P2P
network, computational cost, backhaul bandwidth cost, number of communicated
messages to/from the SDN controller.

     * •

Algorithm performance metrics: execution times and objective function values.

  4. RQ4

How can the utility of the proposed NAVS frameworks be thoroughly evaluated,
both theoretically and practically?
Conducting performance evaluations on large-scale testbeds instead of toy
simulators or numerical assessments offers several advantages, such as a more
realistic representation of real-world scenarios and improved identification
of potential performance bottlenecks. Therefore, in order to address this RQ,
we designed large-scale CloudLab-based Ricci et al. (2014) testbeds to run
realistic network topologies for all proposed frameworks (Chapters 3–6). These
testbeds include hundreds of elements, each of which runs Linux-based
operating systems inside Xen virtual machines. These components include
tens/hundreds of DASH players that run buffer-based and hybrid-based ABR
algorithms, CDN/origin servers that contain bitrate ladders of real video
datasets, the SDN controller and edge servers, which enable to run virtualized
functions, and programmable OpenFlow backbone switches. Moreover, realistic
network traces, tools, and assumptions were used for designing such testbeds.
Additionally, our proposed frameworks were evaluated against state-of-the-art
and baseline approaches using realistic network traces, tools, and
assumptions. The testbed results demonstrated that our proposed frameworks
outperformed their competitors in terms of QoE, network utilization, and
algorithm performance metrics in most scenarios.

###  7.2 Future Directions and Challenges

As discussed throughout this Ph.D. thesis, several techniques have been
utilized in designing NAVS systems to improve the QoE metrics of adaptive
streaming services. However, new challenges have emerged in the video
streaming field that require further attention from the network and service
management perspectives. Therefore, this section identifies some of these
challenges and discusses potential opportunities associated with them.

####  7.2.1 Designing NAVS Systems Considering Traffic Encryption

Privacy and security have become two primary demands for Internet users in
recent years. Therefore, Internet stakeholders and application providers
typically utilize encryption methods, from communication protocols to
application levels, to guarantee users’ privacy and security. This trend
involves video streaming applications as well Nigam et al. (2021). While
traffic encryption can provide private and secure services, it poses severe
difficulties for designing NAVS solutions. This is because most of the NAVS
systems require analyzing users’ exact requests to enhance adaptive video
streams, which may contradict users’ privacy. Therefore, content encryption
brings extra challenges for developing NAVS solutions, such as limited content
visibility and extra latency for encrypted videos, unless the service provider
and the network provider overlap or agree to collaborate. Moreover,
determining, calculating, and estimating the QoE metric necessary to provide
network-based solutions would also be challenging.

Many solutions have been suggested to address this challenge in specific
scenarios. Most utilize machine learning techniques to deduce key QoE metrics
from observable traffic patterns and statistics. For instance, the authors in
Orsolic et al. (2017); Shen et al. (2020); Orsolic and Skorin-Kapov (2020)
presented QoE classification techniques for YouTube encrypted traffic.
However, in the future, more research is necessary to classify encrypted
traffic and evaluate QoE models on in-network components with the highest
possible accuracy and the lowest possible latency, particularly in the context
of live video scenarios. Furthermore, QoE analyses in the presence of Digital
Rights Management (DRM) Ma (2017), which content providers use to restrict
unauthorized distribution, must be performed by in-network components to
investigate whether the service works as intended and not poses any
degradation in the users’ QoE.

####  7.2.2 Designing NAVS Systems Utilizing Emerging Protocols

QUIC (Quick UDP Internet Connection) is a new transport protocol that was
first developed by Google Cui et al. (2017) and is now standardized Iyengar
and Thomson (2021) to address the limitations of the traditional TCP protocol.
Several works showed that QUIC, based on the UDP protocol, can be combined
with networking paradigms to benefit networks and applications like HAS-based
streaming, especially when network congestion, loss, and RTT are high Marx et
al. (2020); Palmer et al. (2018); Kumar and Dezfouli (2021); Hou et al.
(2022). This is because QUIC can: (i) be implemented in the user space rather
than the kernel and can thus be deployed and updated more easily than TCP;
(ii) bring 0-RTT connection establishment when client and server have already
communicated in the past, which helps reducing latency; (iii) provide true
multiplexing of HTTP/2 streams at the transport level, as opposed to standard
TCP that still introduces head-of-line blocking when the packets of a certain
stream are lost. However, devising NAVS solutions over QUIC or supporting
multiple transport protocols (both TCP and QUIC) has only been marginally
investigated and is still an open challenge Farahani et al. (2023a); Chellappa
et al. (2023).

As another promising technology, Web Real-Time Communication (WebRTC) Roach
(2016), including a set of protocols and application programming interfaces
(APIs), has been introduced to allow audio and video streaming directly
between browsers without needing third-party plugins or software. WebRTC can
be used for a variety of use cases, including applications with low-latency
and high-quality requirements, e.g., live streaming. Nonetheless, designing
NAVS solutions that combine HAS-based techniques with WebRTC to improve the
performance and quality of real-time video streaming over the Internet has
only been minimally explored and is still an open challenge.

####  7.2.3 Designing NAVS Immersive Video Streaming

With the popularity and increasing use of immersive streaming devices, Virtual
Reality (VR) and Augmented Reality (AR) content such as point clouds and 360°
video have become a rich and interactive way to consume media. In such types
of streaming, high-resolution content, significant data transfer speed, and
reduced motion-to-glass latency are needed to obtain high-quality and
immersive QoE. However, VR streaming currently suffers from inadequate QoE due
to the extensive bandwidth necessary for immersive content. One technique for
bandwidth reduction and for QoE enhancement of VR streaming is based on
viewport-dependent schemes. The viewport refers to the portion of the content
that the user is currently viewing. The user’s head movements control the
viewport, allowing them to look around the scene in different directions. The
viewport is typically rendered in real-time to provide a seamless and
immersive experience for the user van der Hooft et al. (2023). By dynamically
adjusting the content resolution/bitrate based on the user’s viewport, the
streaming service can reduce the amount of data that needs to be transmitted,
thereby reducing bandwidth consumption and improving the overall quality of
the stream. For instance, when the user is looking straight ahead and not
using peripheral vision, the streaming service can reduce the content
resolution visible in the peripheral vision. In fact, it is less likely that
the user will notice a lower resolution in those areas.

In recent years, video streaming protocols like MPEG DASH have started to use
a new viewport-dependent VR solution called tile-based streaming. In tiled-
based streaming, a video is divided into small rectangular areas, so-called
“tiles”, each of which can be independently streamed based on the user’s field
of view. As the user moves their head and changes their viewport, the
streaming platform loads and renders only the necessary tiles rather than
streaming the entire video van der Hooft et al. (2023). However, problems like
the use of in-network servers, e.g., edge servers, which employ machine
learning techniques to predict user behavior and network conditions to
prefetch and prioritize content, or how to prioritize the delivery of viewport
tiles using networking protocols like HTTP2/3, need further investigations and
are still open challenges in designing NAVS systems for tile-based immersive
video streaming.

####  7.2.4 Designing Sustainable NAVS HAS System

In next-generation networks, particularly in 6G, sustainability will become a
fundamental aspect, aiming to create eco-friendly networks, also known as
green networks Clemm, Futurewei, Westphal, Tantsura, Microsoft, Ciavaglia,
Nokia, Odini (2023); Yrjölä et al. (2020). Sustainable NAVS multimedia systems
refer to the use of network and streaming technologies that improve the
environmental impact while still providing high-quality and low-latency
services to users. This can involve improving the use of network resources,
reducing energy consumption, and decreasing the carbon footprint associated
with video streaming.

However, problems such as (i) leveraging emerging computing technologies to
improve the sustainability and efficiency of video streaming systems, (ii)
using in-network components to assist video encoders in reducing video
bitrates and energy consumption, e.g., by constructing dynamic bitrate ladders
or by deploying lightweight video quality assessment methods Menon et al.
(2023); Vignesh et al. (2023), while maintaining high quality and user
experience, or (iii) enhancing network resource utilizations in multi-CDN
environments Bentaleb et al. (2023), such as bandwidth and server capacity,
and reducing energy consumption and carbon emissions, have rarely been
investigated yet, and are still open challenges in designing sustainable NAVS
systems Afzal et al. (2023).

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