# Evaluation of Packet Wash for Low-Latency High-Bitrate Game Streaming

Mohammadreza Ghafari, Thibault Cholez, Olivier Festor

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Mohammadreza Ghafari, Thibault Cholez, Olivier Festor. Evaluation of Packet Wash for Low-Latency High-Bitrate Game Streaming. EMS 2025 - 3rd Workshop on Emerging Multimedia Systems, SIGCOMM '25: ACM SIGCOMM 2025 Conference, Sep 2025, Coimbra, Portugal. pp.7-12, 10.1145/3746441.3748228 . hal-05375897

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# Evaluation of Packet Wash for Low-Latency High-Bitrate Game Streaming

Mohammadreza Ghafari, Thibault Cholez, Olivier Festor

Université de Lorraine, CNRS, Inria, LORIA

F-54000 Nancy, France

first.last@loria.fr

## ABSTRACT

The rapid advancement of immersive multimedia applications, such as cloud gaming, necessitates streaming technologies that deliver both low-latency and high-bitrate to ensure a seamless Quality of Experience (QoE). Conventional transport protocols like TCP and UDP often fail to simultaneously meet these demands, with challenges such as bufferbloat exacerbating latency or loss events that disrupt real-time experience in case of congestion. To address these limitations, this study evaluates the packet wash mechanism introduced in the Big Packet Protocol (BPP) and made possible by Scalable Video Coding (SVC), tailored for real-time applications like cloud gaming. The packet wash mechanism can discard on-the-fly higher-quality payload layers in network buffers during congestion events, preventing gameplay interruptions without requiring server-side negotiation or re-encoding. This network-based approach minimizes the effects of congestion compared to traditional bitrate adaptation methods. Experimental results for 2K game streaming demonstrate that the packet wash mechanism preserves visual quality with negligible degradation during sudden bandwidth drops.

## KEYWORDS

Big Packet Protocol, Scalable Video Coding, Cloud Gaming, Low-Latency Streaming, Congestion Control

## 1 INTRODUCTION

The rapid proliferation of advanced multimedia applications, such as Cloud Gaming (CG), Augmented Reality (AR), and Virtual Reality (VR), demands future network architectures capable of meeting stringent performance requirements. These

<figure><img src="image_2.png" /><figcaption>**Figure 1: Packet wash avoids packet loss and delay by washing part of payload in the bottleneck queue.**</figcaption></figure>

applications are highly sensitive to latency, jitter, and bandwidth fluctuations, revealing the limitations of conventional transport protocols like TCP and UDP which end-to-end Congestion Control Algorithms (CCA) often fail to avoid bufferbloat [7]. Consequently, there is a pressing need for innovative transport protocols tailored to future networks, ensuring low-loss and low-latency transmission to deliver a high Quality of Experience (QoE).

Adaptive bitrate streaming and Content Delivery Networks (CDNs) are widely used in multimedia applications, dynamically adjusting streams under constrained network conditions. However, these approaches face challenges in low-latency scenarios where large play-out buffers are impractical and storing multiple high-resolution content versions is infeasible due to real-time content generation.

To address these challenges, the Big Packet Protocol (BPP) [12] emerged as a novel solution for future networks to better support high-bandwidth, low-latency applications through in-network packet processing. Unlike UDP, where congestion results in delays and packet loss, BPP introduces the *packet wash* [13] mechanism to mitigate this issue. Packet wash allows applications to define per-packet decision making, optimizing Quality of Service (QoS) regarding their specific throughput and delay objectives. During sudden bandwidth fluctuations, packet wash can dynamically discard less critical payload portions at the bottleneck, accommodating incoming packets with reduced delay and without complete loss, as illustrated in Figure 1. This bitrate adaptation is instantaneous and does not require any client-server interaction. Even the recent Low Latency, Low Loss, and Scalable Throughput (L4S) architecture cannot achieve a bitrate adaptation below 1 Round-Trip Time (RTT) [15].

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EMS '25, September 8-11, 2025, Coimbra, Portugal

© 2025 Copyright held by the owner/author(s).

ACM ISBN 979-8-4007-2061-1/25/09.

https://doi.org/10.1145/3746441.3748228
In this regard, we evaluate the integration of packet wash with high-bitrate Scalable Video Coding (SVC) flows using the H.264 standard [11], enabling selective removal of enhancement layer data in the network, especially at its edge, where congestion usually occurs and advanced packet processing is feasible. It is important to note that this paper does not aim to propose a new video codec with better coding gains, but evaluates the efficiency of packet wash within the widely adopted H.264 codec.

The main contributions of our study are:

(1) Development of handcrafted configurations of SVC enhancement layers for high-resolution game streaming, leveraging quality scalability via Medium Grain Scalability (MGS) to ensure decodability after washing;

(2) Experimentation of BPP's packet wash in a representative scenario involving high-resolution streaming of three genre-diverse games, with sudden bandwidth reductions of up to 50%. All data are available online for reproducibility<fnref n="1" />;

(3) Evaluation of packet wash efficiency showing smooth gameplay, minimal visual degradation validated by modern quality metrics, and performance comparable to server-side bitrate adaptation.

To achieve its objectives, this paper is structured as follows: Section 2 reviews related work, Section 3 presents the packet wash mechanism and its application in CG, Section 4 analyzes packet wash's impact through quality assessments, and Section 5 concludes with future research directions.

## 2 RELATED WORK

CG has gained attraction as a mean to quickly deliver high-quality gaming experiences to diverse clients without requiring powerful hardware. However, it faces challenges in meeting QoS requirements, particularly supporting low-latency. A comprehensive study by Marchal et al. [14] analyzed CG platforms under synthetic and real-world cellular network conditions, examining traffic adaptation strategies to packet loss, reduced throughput, increased latency, and jitter to maintain QoS. The findings highlight the critical role of adaptive traffic management in ensuring a stable user experience under fluctuating network conditions and suggest that an active support of the network would help to enhance the QoS of CG platforms. The following study by Graff et al. [8] evaluated two Active Queue Management (AQM) solutions to preserve CG traffic QoS during network congestion against competing flows. The first leverages the L4S architecture [1] with the DualPI2 AQM and the SCReAM CCA [10], enabling low-latency treatment with application-level support. The second employed Hierarchical Token Bucket (HTB) queuing, prioritizing traffic through pre-classified rules and

network engineering. Experimental results on a controlled CG platform demonstrated that both solutions improved the QoS of CG traffic but have different operational constraints. Pudica [16] proposes an intelligent CCA optimized for CG that probes bandwidth utilization to overcome the slow responsiveness of traditional methods to sudden bandwidth drops. However, although these solutions effectively reduce bufferbloat, they still rely on a congestion signal to be processed by the server, which inevitably introduces a delay, whereas BPP achieves instantaneous adaptation at the bottleneck.

A pioneering effort in next-generation network protocols is presented by Li et al. [12], introducing the BPP as a flexible and programmable framework to meet modern application demands. BPP embeds metadata in IP packet headers, enabling edge network devices to process packets intelligently for flow-specific control, typically by trimming packets dynamically thanks to the packet wash features [13]. Building on this, Dong and Li [6] enhanced transmission control by embedding frame characteristic metadata to prioritize packets during congestion, distinguishing I, P, and B frames for improved decodability. However, this approach may still result in frame loss, potentially disrupting smooth playback. Clayman et al. [4, 5] were the first to leverage SVC to enhance video decodability by mapping multi-layer SVC streams into packets for BPP transmission. Their study explored packetization strategies and their QoE impact, utilizing SVC's resilience to packet loss for robust streaming.

In this regard, we aim to extend their work in several ways. First, we consider high-resolution video streams that are representative of current low-latency high-bitrate applications like CG, which come with additional challenges regarding the SVC strategy. Second, we delve into the encoding procedure of SVC, with a particular focus on Medium-Grain Scalability (MGS), to ensure robustness against decoding errors. Third, we consider better quality metrics than PSNR: SSIM and VMAF, for a more accurate evaluation of user perception, thus we compare BPP achieved quality against a regular server-side bitrate adaptation.

## 3 PROPOSED STREAMING FRAMEWORK

The proposed streaming framework leverages the *packet wash* mechanism to enable dynamic quality adaptation under fluctuating network conditions, ensuring robust and responsive game streaming. This section outlines the key components of the proposed framework: Scalable Video Coding, Payload Construction, and Big Packet Protocol Streaming.

### 3.1 Scalable Video Coding

While it isn't the most advanced codec, SVC H.264 remains the most widely supported codec across existing decoders,

<footnote n="1">¹https://github.com/mrezaghafari/bppয়_স্ট্রোম_ব্যাক্ট</footnote>
making it a practical choice for deployment. Although more
recent codecs like Versatile Video Coding (VVC) offer su-
perior compression gains, scalable VVC software was not
publicly available during this study.

SVC encodes video into a hierarchical structure compris-
ing a base layer and one or more enhancement layers. The
base layer delivers essential video quality for basic playback,
ensuring compatibility and minimal bandwidth usage. En-
hancement layers incrementally improve quality through
spatial scalability (frame size), temporal scalability (frame
rate), or quality scalability (signal-to-noise ratio).

For game streaming, quality scalability is particularly effec-
tive during bandwidth fluctuations. Temporal scalability may
reduce responsiveness, degrading multiplayer experiences,
while spatial scalability often requires device-level adjust-
ments, limiting its practicality. Quality scalability, however,
balances visual quality and bandwidth efficiency, making it
ideal for real-time applications. SVC supports two quality
scalability approaches: Coarse-Grain Scalability (CGS) and
Medium-Grain Scalability (MGS) [9].

**Coarse-Grain Scalability (CGS):**

* A specialized form of spatial scalability that maintains constant resolution, using inter-layer prediction for macroblock modes, motion parameters, and residual signals without up-sampling.

* Supports up to eight discrete quality levels in theory, with each enhancement layer using a finer quantization step size than the previous layer.

**Medium-Grain Scalability (MGS):**

* Provides finer quality adaptation by dividing transform coefficients of macroblocks (e.g., 4×4) into multiple sub-layers within a single enhancement layer.

* Enables smooth adaptation to variable bandwidth, ideal for real-time streaming scenarios.

CGS creates a hierarchical structure with inter-layer de-
pendencies across temporal references. Applying packet
wash to CGS results in a high risk of decoding errors, as
washing temporal frames may disrupt the Group of Pictures
(GoP), leading to visual artifacts due to wrong frame decod-
ing. In contrast, MGS defines quality layers at the macroblock
level within individual frames, decoding each frame from
the base layer to the highest enhancement layers with a
unique quality ID. This independence allows selective decod-
ing based on available bandwidth, guided by packet wash.

**3.2 Payload Construction**

BPP enables structured payload construction for quality
adaptation using Network Abstraction Layer (NAL) units,
which include video coding layers and critical non-video
layers (e.g., Sequence Parameter Set (SPS) and Picture Param-
eter Set (PPS)) defining decoding parameters. Enhancement

<figure><img src="image_3.png" /><figcaption>Figure 2: Payload construction using Even Split (equal embedding) quality level chunks.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3: BPP enables lower-quality streaming without outages under sudden bandwidth drop.</figcaption></figure>

layers may be washed selectively, but non-video layers must
remain intact to ensure playback.

To put VCL NALs into BPP packets, we follow the Even
Split strategy defined in [4]. Figure 2 illustrates payload con-
struction, embedding quality chunks from the base layer
through enhancement layers. BPP’s control unit embeds
metadata in packet headers, allowing edge routers to assess
network conditions and perform packet wash without delay.
This reduces payload size while maintaining decodability.
Since encryption must only protect the payload, routers can
access BPP’s metadata to apply washing safely.

**3.3 Big Packet Protocol Streaming**

Figure 3 compares conventional UDP and BPP streaming,
highlighting BPP’s ability to avoid outages through struc-
tured payloads and dynamic quality adaptation. The stream-
ing approaches are summarized as follows:

* **BPP Streaming:** Supports scalability and quality adaption based on network conditions. Structured payloads enable selective washing of quality layers while preserving decodability.

* **UDP Streaming:** Transmits video data without structured payloads, embedding quality layers as NAL units. Packet drops increase the risk of decoding failures.

It is worth noting that supporting BPP’s packet wash will
likely require software updates at intermediate nodes, which
is made easier today by architectures and languages enabling network programmability, in particular Software-Defined Networking (SDN), Network Function Virtualization (NFV) and Programming Protocol-independent Packet Processors (P4). As with other emerging approaches, typically L4S, BPP may require application-level changes at both sender and receiver to use appropriate codec configuration.

# 4 PERFORMANCE ASSESSMENT

This section evaluates BPP streaming compared to a basic UDP streaming, focusing on gameplay continuity and visual quality under constrained bandwidth conditions.

## 4.1 Experimental Setup

### Dataset

A detailed analysis of video game contents, available in [17], highlights attributes like resolution and framerate; however, uncompressed streams lack 2K resolution support. Other high-quality datasets, such as those in Barman et al. [3], are not publicly available. To address this, high-quality captures of *DevilMayCry*, *HyperUniverse*, and *PES2019* games from the GamingHDRVideoSET dataset<fnref n="2" /> [2] were obtained through direct author contact. The used samples are 10 seconds long. Due to SVC limitations with high-resolution input, the samples were pre-processed to align the uncompressed video game samples with the SVC capabilities as follows:

* **Framerate:** Downsampled from 60 fps to 30 fps using frame sampling in JSVM reference software [11].

* **Color space:** Converted from HDR to 8 bit YUV 4:2:0 format via color mapping for SVC compatibility.

### Scalable Video Coding configuration

SVC was adopted using JSVM reference software [11], encoding the video games into three quality layers: a base layer and two enhancement (enh.) layers. SVC's number of scalable quality layers may depend on coding configurations such as resolution and framerate. Thus to represent low, medium, and high quality, three SVC layers: base, enh.1, and enh.2, were selected for 2K@30fps video games in this study. This structure supports quality adaptation via BPP's packet wash function, configured as follows:

* **Quality scalability mode:** MGS was used, encoding each frame's macroblocks three times to support quality adaptation while applying packet wash.

* **Compression ratio:** Quantization Parameter (QP) values were set at 30 for the base layer, decreasing by 5 (i.e., 25 and 20) for enhancement layers to ensure quality differentiation.

* **Coding structure:** A Group of Pictures (GoP) size of 16 was used, inserting an I-frame every 16 frames, with only P-frames to meet low-latency streaming.

These settings allow BPP to adapt video quality in response to unexpected bandwidth drops while preserving decodability. This capability is achieved at the cost of a moderate bitrate overhead and increased coding complexity compared to single-layer SVC.

### BPP Packet Wash Command

The experiments were conducted locally, relying on packet wash software available in [4]<fnref n="3" />. Three core components were run: *the sender* executes BPP streaming, *the forwarder* applies packet wash, and *the listener* decodes the washed stream. All evaluations were run on an Ubuntu 24.04 LTS machine, equipped with an Intel(R) Core(TM) i7-10700 CPU@2.90GHz, 32 GB RAM, and a GeForce RTX 2080 GPU. Both BPP and UDP pakckets use a size of 1500 bytes. In BPP's structured payload, non-video coding (e.g., SPS and PPS) and the base layer (I and P frames) are preserved for minimum decodability. As shown in Figure 3, enh. layers, excluding their I-frames, were selectively trimmed (washed) as follows:

* **Quality Level 1:** Trims P-frames from enh. layers 1 and 2, targeting temporal layers T1 through T4 (GoP size 2⁴ = 16).

* **Quality Level 2:** Trims P-frames from enh. layer 2 only, targeting temporal layers T1 through T4.

### Quality Assessment metrics

The following metrics assess quality changes after packet wash. Higher values indicate a greater similarity between the compressed/washed stream and the uncompressed content.

* **PSNR:** A common pixel-wise fidelity metric (in dB), though it poorly reflects human perception.

* **SSIM:** Captures luminance, contrast, and structure similarity; ranges from 0 to 1.

* **VMAF:** Better reflects human visual perception; widely used in streaming; ranges from 0 to 100.

## 4.2 Performance Analysis

The analysis compares BPP and UDP for gameplay viability and visual quality under various bandwidth constraints for each game. Each game requires a distinct bandwidth, based on the previously defined QP values, to achieve full-quality streaming, as indicated by the green bars in Table 1. Then, the bandwidth is unexpectedly dropped to the blue and red bars, to achieve QL 2/1, respectively, as shown in Table 1.

4.2.1 *BPP vs UDP Streaming.* Table 1 summarizes the results, leading to the following observations:

<footnote n="2">²github.com/NabajeetBarman/GamingHDRVideoSET</footnote>

<footnote n="3">³github.com/stuartclayman/h264_over_bpp</footnote>
<table><caption>Table 1: Comparison of BPP and UDP streaming across quality levels.</caption><thead><tr><th rowspan="2">Game</th><th rowspan="2">Bandwidth</th><th colspan="2">BPP Streaming</th><th colspan="2">UDP Streaming</th></tr><tr><th>Washed Packet</th><th>Game Outage</th><th>Lost Packet</th><th>Game Outage</th></tr></thead><tbody><tr><td rowspan="3">DevilMayCry</td><td>30Mbps (QL 3)</td><td>0.0%</td><td>0.0s</td><td>0.0%</td><td>0.0s</td></tr><tr><td>21Mbps (QL 2)</td><td>30.8%</td><td>0.0s</td><td>30.0%</td><td>9.8s</td></tr><tr><td>16Mbps (QL 1)</td><td>44.3%</td><td>0.0s</td><td>42.7%</td><td>9.9s</td></tr><tr><td rowspan="3">HyperUniverse</td><td>16Mbps (QL 3)</td><td>0.0%</td><td>0.0s</td><td>0.0%</td><td>0.0s</td></tr><tr><td>13Mbps (QL 2)</td><td>21.2%</td><td>0.0s</td><td>21.2%</td><td>9.6s</td></tr><tr><td>11Mbps (QL 1)</td><td>32.3%</td><td>0.0s</td><td>28.3%</td><td>9.7s</td></tr><tr><td rowspan="3">PES2019</td><td>32Mbps (QL 3)</td><td>0.0%</td><td>0.0s</td><td>0.0%</td><td>0.0s</td></tr><tr><td>25Mbps (QL 2)</td><td>23.3%</td><td>0.0s</td><td>22.5%</td><td>9.9s</td></tr><tr><td>20Mbps (QL 1)</td><td>36.3%</td><td>0.0s</td><td>28.3%</td><td>10s</td></tr><tr><td rowspan="3">Average</td><td>26Mbps (QL 3)</td><td>0.0%</td><td>0.0s</td><td>0.0%</td><td>0.0s</td></tr><tr><td>19Mbps (QL 2)</td><td>25.1%</td><td>0.0s</td><td>24.6%</td><td>9.7s</td></tr><tr><td>16Mbps (QL 1)</td><td>37.7%</td><td>0.0s</td><td>33.1%</td><td>9.8s</td></tr></tbody></table>

* **Washed/Lost Packets:** On average, as bandwidth decreases from full-quality to 16Mbps, washed or lost packets respectively increase from 0.0% to 37.7% (BPP) and 33.1% (UDP).

* **Game Outage:** BPP streaming maintains uninterrupted gameplay across all quality levels, while UDP, in the absence of CCA, suffers from outages during almost the whole sample duration (between 9.6s and 10s over 10s).

4.2.2 *Quality Assessment.* Quality assessment encompasses objective quality metrics and visual impact, as shown in Figures 4-6.

## Objective Quality Assessment

Figures 4, 5 yield the following insights. Note that UDP's inability to handle bandwidth fluctuations natively requires server-side bitrate reduction using SVC Bitstream Extraction (BitExt) [11], introducing reaction time to congestion events.

* **Overall Performance:** All metrics show negligible quality degradation with packet wash, confirming packet wash effectiveness. In particular, VMAF indicates minimal quality degradation across frames, suggesting that packet wash's impact is visually imperceptible.

* **Packet Wash vs BitExt:** Packet wash (edge processing) achieves comparable quality to BitExt (server-side processing) with almost no quality loss for Quality Levels 2-3, and a minimal quality loss at the lowest bitrate.

* **Lowest Quality Analysis:** The yellow regions in Figure 4 highlights packet wash's inefficiency compared to BitExt. Packet wash retains I-frames while discarding P-frames of the enh. layers. However, BitExt selectively preserves the essential P-frames rather than retaining all I-frames, resulting in better quality.

## Visual Quality Impact

<figure><img src="image_5.png" /><figcaption>Figure 4: Average objective quality metrics of game streaming subject to packet washing, from top to bottom: VMAF, SSIM and PSNR.</figcaption></figure>

Figure 6 (with exaggerated blobs for better visibility) supports the objective findings, with the following observations:

* Most regions of the video stay at the highest quality level.

* Quality degradation, resulting from washing enh. layers of P-frames is more noticeable in high-motion regions.

# 5 CONCLUSION

This study demonstrates the efficacy of BPP in enhancing real-time streaming for cloud gaming through packet wash
<figure><img src="image_6.png" /><figcaption>Figure 5: VMAF score per frame, averaged over games.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 6: Blobs highlight regions affected by packet wash compared to full quality reference.</figcaption></figure>

mechanism. Operating at the network edge, packet wash can handle sudden congestion without client-server interaction to process congestion signal, effectively managing network congestion with no delay and with minimal quality degradation. Comparative analysis reveals that BPP ensures uninterrupted gameplay by adaptively washing enhancement layer payloads during bandwidth fluctuations, whereas UDP streaming alone suffers significant outages. Future work will study packet wash integration with more recent codecs like Scalable HEVC, with a focus on evaluating the reduction of queuing latency compared to other promising network architectures, particularly L4S.

# 6 ACKNOWLEDGEMENT

This work is partially funded by the French ANR PEPR NF-NAI project, No ANR-22-PEFT-0003. We would like to thank the authors of [4] for their valuable support during this study.

# REFERENCES

[1] O. Albisser et al. 2019. DualPI2: Low latency, low loss, and scalable (L4S) AQM. In *Netdev 0x13*. Prague, Czech Republic.

[2] N. Barman and M. G. Martini. 2022. 4K HDR gaming dataset for subjective and objective quality assessment. *IEEE Transactions on Circuits and Systems for Video Technology* 32, 3 (Mar. 2022). https://doi.org/10.1109/TCSVT.2021.3068732

[3] N. Barman et al. 2024. Codec compression efficiency evaluation for ultra low-latency cloud gaming applications. In *IEEE International Conference on Quality of Multimedia Experience*. Karlshamn, Sweden.

[4] S. Clayman and M. Sayit. 2023. Low latency low loss media delivery utilizing in-network packet wash. *Journal of Network and Systems Management* 31, 2 (2023). https://doi.org/10.1007/s10922-022-09712-1

[5] S. Clayman et al. 2024. Enhancing Bandwidth Utilization for Video Streaming with Packet Trimming in 6G. In *20th International Conference on Network and Service Management (CNSM)*. 1-5. https://doi.org/10.23919/CNSM62983.2024.10814413

[6] L. Dong and R. Li. 2024. Characteristics Aware Video Packet Dropping and Packet Wash. In *International Conference on Computing, Networking and Communications (ICNC)*. Big Island, HI, USA, 591-597. https://doi.org/10.1109/ICNC59896.2024.10556051

[7] J. Gettys and K. Nichols. 2011. Bufferbloat: Dark Buffers in the Internet: Networks without Effective AQM May Again Be Vulnerable to Congestion Collapse. *Queue* 9, 11 (11 2011), 40-54. https://doi.org/10.1145/2063166.2071893

[8] P. Graff et al. 2024. Improving Cloud Gaming traffic QoS: a comparison between class-based queuing policy and L4S. In *2024 8th Network Traffic Measurement and Analysis Conference (TMA)*, IFIP (Ed.). IEEE, Dresden, Germany, 10. https://doi.org/10.23919/TMA62044.2024.10558920

[9] R. Gupta et al. 2012. H.264 coarse grain scalable (CGS) and medium grain scalable (MGS) encoded video: A trace based traffic and quality evaluation. *IEEE Transactions on Broadcasting* 58, 3 (September 2012). https://doi.org/10.1109/TBC.2012.2199388

[10] I. Johansson. 2014. Self-clocked rate adaptation for conversational video in LTE. In *ACM SIGCOMM Workshop on Capacity Sharing Workshop (CSWS)*. Chicago, IL, USA. https://doi.org/10.1145/2630088.2630096

[11] JSVM. 2025. Access:. vcgit.hhi.fraunhofer.de/jvet/jsvm. (2025).

[12] R. Li et al. 2018. A new framework and protocol for future networking applications. In *Proceedings of the Workshop on Networking for Emerging Applications and Technologies*. Budapest, Hungary. https://doi.org/10.1145/3229574.3229577

[13] R. Li et al. 2019. A Framework for Qualitative Communications Using Big Packet Protocol. In *Proceedings of the ACM SIGCOMM 2019 Workshop on Networking for Emerging Applications and Technologies (NEAT'19)*. Association for Computing Machinery, New York, NY, USA, 22-28. https://doi.org/10.1145/3341558.3342201

[14] X. Marchal et al. 2023. An Analysis of Cloud Gaming Platforms Behaviour Under Synthetic Network Constraints and Real Cellular Networks Conditions. *Journal of Network and Systems Management* 31, 2 (Jan. 2023), 39. https://doi.org/10.1007/s10922-023-09720-9

[15] K. De Schepper and B. Briscoe. 2023. The Explicit Congestion Notification (ECN) Protocol for Low Latency, Low Loss, and Scalable Throughput (L4S). RFC 9331. (Jan. 2023). https://doi.org/10.17487/RFC9331

[16] S. Wang et al. 2024. Pudica: toward near-zero queuing delay in congestion control for cloud gaming. In *Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI'24)*. USENIX Association, USA, Article 7, 17 pages.

[17] X. Yu et al. 2024. Subjective and objective analysis of streamed gaming videos. *IEEE Transactions on Games* 16, 2 (June 2024). https://doi.org/10.1109/TG.2023.3296952