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# QoE Evaluation of BPP Packet Wash Using ROI-based Scalable Video Coding

Mohammadreza Ghafari, Thibault Cholez, Olivier Festor

► To cite this version:

Mohammadreza Ghafari, Thibault Cholez, Olivier Festor. QoE Evaluation of BPP Packet Wash Using ROI-based Scalable Video Coding. ISM 2025 - 27th IEEE International Symposium on Multimedia, Dec 2025, Naples, Italy. pp.1-8. hal-05424680

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https://inria.hal.science/hal-05424680v1

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# QoE Evaluation of BPP Packet Wash Using ROI-based Scalable Video Coding

Mohammadreza Ghafari, Thibault Cholez, and Olivier Festor

Université de Lorraine, CNRS, Inria, LORIA

F-54000 Nancy, France

first.last@loria.fr

**Abstract**—Latency-sensitive multimedia applications, such as cloud gaming, require advanced streaming solutions that provide both low latency and high visual quality to ensure a seamless user experience. Conventional transport protocols often struggle under sudden bandwidth fluctuations, resulting in latency spikes, packet loss, and ultimately gameplay interruptions. To address these limitations, this work explores the capabilities of the Big Packet Protocol (BPP) packet wash mechanism combined with Region of Interest (ROI) coding by Scalable Video Coding (SVC) for 2K video games to satisfy the Quality of Experience (QoE) under network congestion. BPP packet wash dynamically discards non-critical enhancement layer chunks at the network edge during congestion, ensuring a critical Quality of Service (QoS)-preserving remediation before the application's Control Congestion Algorithm (CCA) comes into play to adapt the bitrate. Comparative experiments for various coding strategies after applying packet wash show that ROI SVC can handle bandwidth drops more efficiently, up to 52% reduction, while still maintaining uninterrupted gameplay and satisfactory visual quality in the most critical regions of the game, according to QoE evaluation involving real users. These results indicate that packet wash with ROI SVC provides an effective solution for real-time interactive multimedia streaming, such as cloud gaming.

**Index Terms**—Low Latency, Big Packet Protocol, Packet Wash, Region of Interest, Scalable Video Coding, Quality of Experience

## I. INTRODUCTION

In the era of digital entertainment, video gaming hardware is evolving from its traditional form, once limited to consoles or local computers, into cloud-based platforms, commonly referred to as *cloud gaming*. Cloud gaming allows gamers to access high-quality video gaming without worrying about their device's computational power, from anywhere they want, only requiring a good Internet connection. However, this evolution has raised some technical challenges for multimedia network streaming, particularly how to satisfy the required Quality of Service (QoS). Naturally, these challenges have also raised some concerns among gamers about whether they can fully rely on cloud gaming while maintaining a satisfactory Quality of Experience (QoE). Cloud gaming requires high visual quality implying high data rate, together with extremely low latency and reliable network conditions, as even minor delays or packet losses can severely disrupt the user experience. Unlike regular video playback, such as streaming movies, where buffering and re-transmission are possible, every frame in cloud gaming is critical and must be transmitted in real time

to maintain interactivity. Any loss can cause missed inputs or visual artifacts, immediately compromising immersion.

Acknowledgement-based protocols such as TCP are not suitable for cloud gaming, as they violate its real-time requirement. Conversely, relying on UDP necessitates to use an appropriate Congestion Control Algorithm (CCA) to deal with unmanaged network conditions. On the other hand, CCAs depend on congestion notifications from the network edge to the sender to adjust the rate based on the available network budget, what inherently introduces a reaction delay, ultimately degrading the QoE. However, the recently developed solution, Low Latency, Low Loss, Scalable Throughput (L4S) architecture, introduces a better approach than conventional solutions, but still cannot achieve bitrate adaptation less than 1 Round Trip Time (RTT) [1].

To address these limitations, the Big Packet Protocol (BPP) was introduced as “A New Framework and Protocol for Future Networking Applications” [2]. It represents a pioneering approach to in-network packet processing, specifically designed for high-throughput and ultra-low latency communications. Unlike traditional protocols that treat each packet as an in-divisible unit, BPP encapsulates data into multiple prioritized chunks, each carrying metadata about its significance. This enables intermediate programmable routers to make fine-grained decisions, especially trimming (“washing”) less important chunks under congestion instead of dropping the entire packet, what is called as “BPP packet wash mechanism” [3]. Such programmability opens the door to qualitative communications, especially for real-time video streaming, where applications can ensure that the most critical information (e.g., base video layers) is always delivered on time while guaranteeing the full visual quality when enough bandwidth is available. Studying the capabilities and challenges of BPP is particularly valuable for multimedia experts, as it can help accelerate the standardization process of new emerging protocols.

To satisfy this objective, we explore the benefits of integrating BPP with Region of Interest (ROI) technique in Scalable Video Coding (SVC), enabling more flexible quality adaptation under limited bandwidth. It is important to note that this work does not aim to propose a new video codec with improved coding efficiency; it evaluates the effectiveness of packet wash within the widely adopted H.264 standard, similar to [3], [4]. The main novelties of this paper are:

* **Reducing Bandwidth Usage:** Significantly reduce the
minimum required bandwidth achieved by the packet wash mechanism, especially valuable under severe network constraints. This goes beyond what standard SVC with packet wash achieves, as demonstrated in our previous work [4] by applying the ROI technique.

* **Preserving Visual Quality:** Maintain satisfactory visual quality under very limited bandwidth, while ensuring high quality in the most critical regions of the game. This is achieved through game-specific maps during SVC encoding, enabling smarter BPP packet wash.

* **Evaluating QoE:** Subjectively assess the impact of the BPP packet wash mechanism on visual quality through a QoE study with non-expert participants, to evaluate how much in-network BPP packet wash processing affects their viewing experience.

To achieve these objectives, this paper is organized as follows: Section II reviews recent work on cloud gaming, BPP, and video coding. Section III presents the proposed streaming framework, detailing how BPP is utilized for streaming. Section IV describes the experimental setup, ensuring a fair and meaningful comparison. Section V evaluates the experimental results and provides an in-depth analysis of various encoding scenarios, including SVC ROI with BPP packet wash<fnref n="1" />. Finally, Section VI concludes the paper.

## II. RELATED WORKS

Low latency streaming has been extensively studied due to its critical role in interactive multimedia applications such as cloud gaming. In this context, Marchal et al. [5] analyzed various cloud gaming platforms under synthetic and real-world cellular network conditions, examining traffic adaptation strategies in response to packet loss, reduced throughput, increased latency, and jitter. Following this work, Graff et al. [6] studied Active Queue Management (AQM) techniques to maintain QoS under network congestion, comparing (i) the L4S architecture with DualPI2 AQM and SCReAM CCA [1], which provides low latency performance with application-level support, and (ii) Hierarchical Token Bucket (HTB) queuing, which prioritizes traffic based on predefined rules. While both methods improve QoS, L4S offers scalable and responsive adaptation, whereas HTB relies on static configurations and prior traffic knowledge. Graff et al. [7] further proposed machine learning models to identify cloud gaming flows, enabling more effective QoS management.

Pudica [8] introduced an intelligent CCA for cloud gaming that dynamically probes bandwidth to respond quickly to sudden drops, reducing frame delays and stalls while maintaining high visual quality. However, like other methods, it relies on congestion signals processed by the server, introducing inevitable reaction delays. In contrast, BPP [2] enables instantaneous adaptation directly at the network bottleneck, providing immediate responsiveness to congestion, suitable for interactive streaming. BPP addresses unmanaged network bottlenecks through its packet wash mechanism, embedding

metadata in IP packet headers to allow routers to intelligently process packets for flow-specific control, dynamically trimming packets' payloads as needed.

Building on this concept, Clayman et al. [3], [9] pioneered integrating BPP packet wash with Scalable Video Coding (SVC). Their method preserved the base layer while washing only higher enhancement (enh.) layers, enabling real-time bitrate adaptation through quality adaptation. Despite its effectiveness, these studies were limited to very low resolution, reducing their relevance for modern cloud gaming that typically requires high resolutions to satisfy the QoE. Moreover, SVC layer interdependencies can lead to severe decoding artifacts, such as flickering, if an unsuitable scalability model is applied, what was not studied well in the previous works.

To address these limitations, the BPP packet wash mechanism was extended to [4]: i) support high resolution video streams, representative of low latency, high bitrate applications such as cloud gaming; ii) incorporate the SVC encoding procedure with Medium Grain Scalability (MGS) in the SNR scalability mode to enhance decoding error resiliency after packet wash; and (iii) evaluate the potential visual quality loss after packet wash using perceptually aligned metrics such as VMAF, rather than relying solely on PSNR as in previous studies [3], [9]. BPP packet wash performance was compared against conventional server-side bitrate adaptation, demonstrating the effectiveness of BPP packet wash, able to adjust quality without re-encoding or any server-side negotiation.

In cloud gaming, visual importance is not uniformly distributed; players focus on specific regions [10] more than others, which reinforces the adoption of Region of Interest (ROI). ROI solution has been widely explored by different techniques, especially to extract the foreground motions from the background. In this regard, the Mixture of Gaussians (MOG2) algorithm was employed to separate moving objects from the background, enabling more efficient streaming [11]. This approach was later refined with a color clustering technique to improve motion mask accuracy and compression performance [12]. ROI solution in SVC has been adopted as well for different purposes. In this regard, [13] introduced a scalable ROI, allowing the ROI area to adapt its size and position according to the enh. layer resolution, which is needed for the spatial scalability principle. Similarly, Lee and Yoo [15] proposed enhancing the visual quality only for the ROI in the enh. layers, minimizing bitrate overhead. While efficient in rate, this approach cannot guarantee full visual quality for the entire video frame, leaving non-critical regions at poor quality; thus, its performance is dependent on accurate ROI definition.

Despite these advances, prior works have not yet explored the potential benefits of combining ROI-based SVC with the BPP packet wash mechanism, which may enable greater bandwidth savings by allowing more aggressive packet wash under constrained network conditions, while still preserving high quality in the most important regions.

<footnote n="1">¹The results will be in: github.com/mrezaghafari/BPP_using_ROI_SVC</footnote>
### III. PROPOSED STREAMING FRAMEWORK

The proposed streaming framework integrates the BPP packet wash [2] with ROI SVC, to enable dynamic quality adaptation under constrained network conditions. It consists of five key components: (i) *Scalable Video Coding* for multi-layer encoding to embed different quality levels within the SVC bitstream; (ii) *Region of Interest Designing* to prioritize perceptually significant regions important to gamers, during encoding; (iii) *Encoding Scenarios*: to compare the impact of different video coding solutions when applying packet wash, especially with and without ROI; (iv) *BPP Packetization Procedure* for in-network packet processing, embedding important metadata to the BPP packets to process them in the network; and finally, (v) the *Streaming Framework*: BPP streaming, to evaluate its performance under varying network conditions.

#### A. Scalable Video Coding

Scalable Video Coding uses layered coding strategy to adapt video efficiently to varying network conditions. While the recent advanced video codec, Versatile Video Coding (VVC)/H.266 standard, offers superior compression efficiency, its substantially higher computational complexity limits widespread adoption across heterogeneous devices. The complexity of High Efficiency Video Coding (HEVC)/H.265 standard is lower than VVC, but still demands considerable processing power and has more limited device compatibility. In contrast, the Advanced Video Coding (AVC)/H.264 standard, particularly its SVC extension, remains prevalent due to its moderate complexity and broad device support. Among SVC's scalability modes, Medium Grain Scalability (MGS), provides fine-grained quality adaptation that is particularly well suited for in-network packet processing; a capability not yet available in Scalable HEVC, which only supports SNR scalability through Coarse Grain Scalability (CGS) [14], to the best of the authors' knowledge. Furthermore, no scalable extension of VVC was publicly available at the time of this study, reinforcing the selection of SVC as a practical choice.

Building on this rationale, MGS was adopted from SVC to deliver full visual quality when enough bandwidth permits, while enabling *on-the-fly* quality adaptation at the network edge under bandwidth constraints without re-encoding or any edge-server negotiation, achieved through the packet wash mechanism. The BPP packet wash mechanism may be improved by differentiating the video frame regions, especially the ones that are more important to the gamers, which under scores the ROI design procedure.

#### B. Region of Interest Designing

Although ROI can be defined in various ways, a common approach is Flexible Macroblock Ordering (FMO) [15], which partitions frames into rectangular macroblock groups, typically marking the foreground as ROI and the background as non-ROI. However, this method restricts ROI shapes to vertical or horizontal blocks, especially when the large and centered ROIs produce sharp block boundaries that create noticeable visual artifacts. On the other hand, this method faces challenges

when applied to SVC at high resolutions, where FMO cannot be properly defined or efficiently utilized. To the best of the authors' knowledge, this limitation prevents a straightforward adaptation of the FMO-based ROI to high-resolution video formats such as 2K resolution. To address this issue, elliptical ROI masks with the Gaussian blurring technique (gblur [16]) are proposed instead of the traditional FMO-based approach to reduce the high frequency details in non-ROI, thus working as a low pass filter. These masks offer greater flexibility, better align with modern 16:9 aspect ratios, and create smoother transitions with fewer artifacts. The curved boundaries are less perceptible to the human visual system than vertical boundaries, thus reducing edge prominence.

The gblur masks are generated using a single-pass Gaussian approximation (steps = 1) with $\sigma = 4$, corresponding to the standard deviation of the Gaussian kernel as defined in [16]. Mathematically, the gblur of an image $I(x, y)$ is defined as a convolution operation. Let $I(x, y)$ denote the intensity of the pixel at spatial location $(x, y)$ in the input image. The gblur value at this position, $I_{blurred}(x, y)$, is obtained by a weighted sum of neighboring pixels:

$$I_{gblur}(x, y) = \sum_{i=-k}^{k} \sum_{j=-k}^{k} I(x - i, y - j) G(i, j), \quad (1)$$

where $i$ and $j$ denote horizontal and vertical offsets from the central pixel $(x, y)$, and the 2D Gaussian kernel weights, $G(i, j)$, are given by:

$$G(i, j) = \frac{1}{2\pi\sigma^2} \exp\left(-\frac{i^2 + j^2}{2\sigma^2}\right). \quad (2)$$

The size of the kernel is determined from the blur radius $r$ as:

$$r = 2\sigma, \quad \text{kernel size} = [4\sigma + 1], \quad (3)$$

where $[\cdot]$ denotes the ceiling function, and the half-size is $k = \frac{\text{kernel size} - 1}{2}$. For the ROI-based gblur in this work, $\sigma = 4$ is chosen which gives:

$$r = 2 \cdot 4 = 8,$$ 
$$\text{kernel size} = [4 \cdot 4 + 1] = 17,$$ 
$$k = \frac{17 - 1}{2} = 8.$$

Thus, the kernel covers a $17 \times 17$ neighborhood pixels, corresponding to offsets $i, j \in [-8, 8]$ in each direction. Each pixel $(x, y)$ is therefore replaced by a Gaussian weighted average of itself and its surrounding $17 \times 17$ neighborhood. So, for $\sigma = 4$, the kernel weights take the form:

$$G(i, j) = \frac{1}{32\pi} \exp\left(-\frac{i^2 + j^2}{32}\right), \quad i, j \in [-8, 8]. \quad (4)$$

Higher values of $\sigma$ result in stronger blurring and greater visual impact; thus, it also offers the trade-off between bitrate and quality, in addition to typical SVC configurations.

Finally, two gblur ROI masks have been designed and applied while using SVC as follows:

* **Elliptical ROI:** A single centered elliptical ROI mask.
<figure><img src="image_3.png" /><figcaption>Fig. 1. From left to right: game, game map (red boxes), Elliptical ROI, and Elliptical & Game Map ROI (black regions); white regions indicate gblur.</figcaption></figure>

* **Elliptical & Game Map ROI:** A centered elliptical ROI combined with game maps ROI, where the latter covers regions containing essential game information. While the game map has been defined manually in this study, it can ideally be defined by the game designer.

To ensure a fair comparison, the centered area of the elliptical ROI in the second strategy is reduced so that both strategies yield the same overall masked ROI area with respect to the frame size, as shown in Fig. 1.

## C. Encoding Scenarios

To satisfy the objectives described in Section I, the following encoding scenarios are considered:

* **Standard SVC (STD SVC):** The Standard SVC encoding with a base layer and multiple enh. layers.

* **SVC with Bitstream Extractor (SVC w. BitExt):** The standard SVC bitstream is utilized to extract a lower quality stream at a target bitrate at the server side, rather than applying packet wash at the network edge when bandwidth is limited.

* **Elliptical ROI SVC (ROI SVC(Ellip.)):** The SVC encoding with defining a centered elliptical ROI per frame, in both base and enh. layers, where only the center region is encoded at higher visual quality, while the remaining frame is encoded at lower quality; the final enh. layer encodes the full frame at maximum quality, treating the entire frame as ROI.

* **Elliptical & Game Map ROI SVC (ROI SVC(Ellip.&Map)):** Extending the previous approach by defining both centered and game maps ROI (thus more varieties of ROIs) across the base layer and enh. layers; the final enh. layer encodes the full frame at maximum quality, treating the entire frame as ROI.

The resulting bitstream must be packetized according to the BPP format, organizing the SVC layers within each payload.

## D. BPP Packetization Procedure

Once the scalable bitstream is generated, BPP segments and encapsulates the video coding layers, arranging SVC layers within network packets carried as the payload of BPP packets.

<figure><img src="image_4.png" /><figcaption>Fig. 2. BPP packet: BPP encapsulates different SVC quality levels.</figcaption></figure>

Additional BPP metadata is inserted into the packet header, in addition to the standard source and destination IP addresses, as shown in Fig. 2. This structure enables fine-grained control at the network level, allowing higher quality levels to be selectively trimmed (or kept) immediately and without imposing any delay by accessing the BPP metadata, particularly during network congestion, while preserving stream decodability.

BPP constructs each packet by accessing the Network Abstraction Layer (NAL) units from the bitstream as defined in the H.264 standard, incorporating both Video Coding Layers (VCL) and essential non-VCL units such as the Sequence Parameter Set (SPS) and Picture Parameter Set (PPS). While VCL enh. layers can be selectively washed to reduce bandwidth, non-VCLs must remain intact to ensure correct decoding and playback. Each BPP packet has been defined with a fixed size of 1500 bytes, consisting of IP header (20 bytes), UDP header (8 bytes), BPP block header (4 bytes), BPP command block (7 bytes), and the metadata blocks for each chunk (6 bytes per chunk), as defined in [3] and shown in Fig. 2. The remaining bytes in the packet are dedicated to the payload, notably the SVC chunks. The payload is organized using the Even Split strategy proposed by [3] and subsequently used in [4], which distributes VCLs evenly so that each packet contains proportional quality contributions from the base layer up to the highest enh. layers.

BPP's metadata allows edge routers to monitor network conditions and perform real-time trimming of the unnecessary part of the payload when needed. Since encryption applies only to the real data (payload), routers can still access metadata safely to decide where and how much to trim, ensuring optimal adaptation without compromising decodability.

## E. Streaming Framework

Prior works [4], [6] showed that using UDP is prone to temporary packet loss during sudden and significant bandwidth reduction events (common in cellular networks), leading to game outages during the time needed by the CCA to trigger server-side bitrate adaptation. This occurs because UDP transmits packets without considering quality levels, thereby requiring the edge-server negotiation to encode the video at higher compression ratios in order to reduce the bandwidth, which naturally imposes significant delay. In contrast, BPP streaming does not rely on edge-server negotiation. Instead, BPP structures each packet to include a balanced representation of multiple quality levels. This design enables real-time quality adaptation through the packet wash mechanism, where enh. layers can be selectively trimmed from the payload during congestion without compromising decodability. As a result, BPP achieves quality adaptation at line rate by programmable
<figure><img src="image_5.png" /><figcaption>Fig. 3. Proposed streaming framework: BPP packet wash trims the payload intelligently to avoid queuing or packet drop at the network bottleneck.</figcaption></figure>

network hardware (e.g., P4 switch), allowing uninterrupted gameplay even under abruptly changing network conditions. Fig. 3 illustrates the BPP streaming framework, particularly under different bandwidth drops.

It is important to note that, similar to other emerging network technologies, such as L4S, the deployment of BPP may require updates at both the application and network levels to fully leverage its adaptive capabilities. Indeed, BPP's packet wash necessitates the interpretation of the BPP metadata in network equipment, most probably at the network edge, where congestion usually happens. This can be made possible thanks to programmable network technologies (SDN, NFV, P4).

## IV. EXPERIMENTAL SETUP

This section details the experimental setup used to evaluate streaming performance, explaining (i) Test Materials, (ii) Coding Configurations, (iii) BPP Packet Wash Command, and (iv) Quality Assessment Metrics. All experiments were performed on an Ubuntu 24.04 LTS system equipped with an Intel® Core™ i7-10700 CPU @ 2.90 GHz, 32 GB of RAM, and an NVIDIA GeForce RTX 2080 GPU. The experiments were conducted locally to assess video quality following packet wash processing, using the packet wash software available in [17]. The experimental setup, as illustrated in Fig 3, consisted of three main components, from left to right: the *sender*, to stream the BPP packets; the *forwarder*, which applies the packet wash at the network bottleneck under constrained bandwidth; and the *listener*, which decodes the processed washed stream at the client side.

### A. Test Material

Acquiring uncompressed 2K resolution video game footage suitable for evaluating the packet wash mechanism presented a first challenge. This includes not only finding uncompressed test video games but also ensuring a diverse selection of games spanning different genres, visual characteristics, and screen dynamics. Although prior analyses of gaming content, such as [18], offer insights into attributes like resolution and frame rate, the available uncompressed streams do not support 2K resolution. Additionally, other high quality datasets, such as those presented by Barman et al. [19], are not publicly available, further restricting the available options. To address this limitation, we adopt high quality game captures from a wide range of game genres proposed in the GamingHDRVideoSET dataset [20], [21], ensuring a diverse selection of games to assess the impact of ROI SVC under different gameplay scenarios, followed by BPP packet wash. Specifically, Call

of Duty (COD) is shown in the first row of Fig. 1, *Devil May Cry* (DEVIL) in the second row, *Forza Horizon* (FORZA) in the third row, and *Pro Evolution Soccer* (PES) in the fourth row, were selected to assess the impact of BPP packet wash.

### B. Coding Configuration

All tests were SVC encoded/decoded using the JSVM reference software version 9.19.15 [22]. Each 2K@30fps video game was encoded into a base layer and two enh. layers, corresponding to low, medium, and high quality, respectively. The encoder was configured with the following parameters for both ROI-based and non-ROI SVC encoding:

* **Scalability mode:** MGS was employed, encoding each frame's macroblocks three times. This facilitates quality adaptation and enables BPP to assign distinct quality IDs to the base and enh. layers.

* **Quantization Parameter (QP):** The base layer used a QP of 30, while the enh. layers were encoded with QPs of 25 and 20, providing higher quality differentiation.

* **Coding structure:** I-frames inserted every 16 frames (GoP=16), all remaining frames encoded as P-frames. B-frames were omitted to support low latency streaming.

P-frames are used to compress temporal data by encoding only the motions instead of encoding every reference frame independently (I-frames). Besides the above settings, additional configurations were applied per encoding scenario:

* **STD SVC:** Follows the configuration defined in our previous work, available online in [23].

* **SVC w. BitExt:** Utilizes the same bitstream as STD SVC; however, the BitStreamExtractor [22] is employed to generate a lower quality stream at the target bitrate.

* **ROI SVC(Ellip.):** Applies a centered elliptical mask covering 25% of the frame for the base layer and 50% for enh. layer 1, while enh. layer 2 uses a full frame ROI (i.e., no masking). Remaining configurations follow [23].

* **ROI SVC(Ellip.&Map):** Combines a centered elliptical mask with a game map, covering 25% of the frame for the base and 50% for enh. layer 1, with enh. layer 2 using a full frame ROI. Remaining configurations follow [23].

### C. BPP Packet Wash Command

The BPP packet wash command specifies how the payload is optionally trimmed, enabling dynamic quality adaptation in response to network bandwidth limitations.

To ensure decodability, the SPS and PPS must be reliably delivered to the decoder, as they contain essential configuration data required for video decoding. Given their relatively small size, these parameters can be transmitted successfully even under constrained bandwidth conditions. The BPP packet wash command is designed to preserve these parameters along with the base layer, including I- and P-frames, thereby ensuring minimum quality and decodability. Meanwhile, higher quality enh. layers' P-frames are selectively trimmed from the payload at the network edge, enabling the generation of "washed streams" with the following three target quality levels:
* **Quality Level 1:** P-frames of enh. layers 1 and 2 were trimmed from the payload, targeting temporal data T1 through T4 (GoP size $2^4 = 16$).

* **Quality Level 2:** P-frames of only enh. layer 2 were trimmed from the payload, targeting temporal data T1 through T4.

* **Quality Level 3:** P- and I-frames of all layers were retained (i.e., no trimming) to preserve full quality when sufficient bandwidth is available.

## D. Quality Assessment Metrics

Conventional fidelity metrics such as PSNR and SSIM are not well-suited for evaluating ROI-based encoding, as they measure overall fidelity without considering the perceptual importance of regions. VMAF was chosen as it provides a better perceptually aligned assessment than traditional metrics; however, it may still fail to fully capture ROI-specific quality variations. In this regard, the Learned Perceptual Image Patch Similarity (LPIPS) [24], using AlexNet [25] as its backbone, was employed, as it better models human perception and was trained considering perceptual judgements, notably Just Noticeable Differences (JND) [24]. This makes LPIPS particularly effective for assessing perceptual quality in ROI-based scenarios. In this study, LPIPS was computed for each frame, then averaged over all frames of the video game sequence.

Nonetheless, the most reliable approach remains subjective evaluation through the Mean Opinion Score (MOS), conducted in accordance with ITU-T P.910 methodology [27]. A total of 24 participants (14 men and 10 women) were selected from non-experts; the evaluation procedure was designed as follows:

* **Pre-test calibration:** Participants were shown two representative samples for each video game to illustrate the boundaries:

- Losslessly compressed video, QP = 0 (score = 5)

- Heavily compressed video, QP = 40 (score = 1)

* **Main test:** Participants rated all experimental sequences on a discrete 1–5 scale for each encoding scenario, defined in Section III-A.

* **Presentation:** Overall, 48 sequences were shown (4 games × 4 encoding scenarios × 3 quality levels = 48), with a 10 second time interval between sequences to allow sufficient rating time.

The collected MOS scores, together with objective metrics, were used to comprehensively evaluate the impact of the proposed packet wash mechanism applied for each encoding scenario, notably SVC with and without ROI.

## V. PERFORMANCE ASSESSMENT

This section analyzes various BPP streaming under bandwidth constraints to assess the packet wash mechanism.

### A. Bitrate Analysis

BPP streaming is analyzed using standard and ROI-based encoding. Table I summarizes the results and highlights the following overall key insights:

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. VMAF and LPIPS RD performance, averaged over entire games.</figcaption></figure>

* **Bandwidth Usage:** Both STD and ROI SVC strategies dynamically adapt their streams at the network edge without encoder intervention, adjusting quality levels to match the available bandwidth. On average STD SVC bitrate ranges from 37Mbps to 20Mbps, while ROI SVC maintains comparable quality between 36Mbps and 17Mbps, showing a superior bandwidth efficiency.

* **Washing Capability:** Both STD and ROI SVC strategies can dynamically trim portions of the BPP packet payload at the network edge, based on available network budget, without affecting decodability. This effect is more pronounced for ROI SVC (up to 51.8%) on average compared to STD SVC (up to 44.9%), demonstrating the superior capability of ROI SVC over STD SVC and its capacity to sustain bandwidth drops without outage.

* **Benefit of ROI SVC:** ROI SVC achieves target quality levels with lower bandwidth requirements ($\Delta$Bandwidth: 1Mbps to 4.4Mbps) and greater packet wash capability ($\Delta$Washed: 0% to 10.1%) compared to STD SVC.

The results highlight that, although ROI SVC uses the same QP as STD SVC, it achieves higher packet wash capability. This makes it more bandwidth-efficient and better able to prevent gameplay outages during sudden bandwidth drops.

### B. Quality Assessment

The following observations can be made on average for objective and subjective quality assessments:
<table><caption>TABLE I
BPP STREAMING ACROSS VARIOUS ENCODING SCENARIOS; COLORS INDICATE QUALITY LEVELS: GREEN = LEVEL 3, BLUE = LEVEL 2, RED = LEVEL 1.</caption><thead><tr><th rowspan="2">Game</th><th colspan="2">STD SVC</th><th colspan="2">ROI SVC(Ellipt.)</th><th colspan="2">ROI SVC(Ellipt.&Map)</th><th colspan="2">Δ (ROI (Ellipt.) - STD)</th><th colspan="2">Δ (ROI (Ellipt.&Map) - STD)</th></tr><tr><th>Bitrate</th><th>Washed</th><th>Bitrate</th><th>Washed</th><th>Bitrate</th><th>Washed</th><th>Δ Bitrate</th><th>ΔWashed</th><th>Δ Bitrate</th><th>ΔWashed</th></tr></thead><tbody><tr><td rowspan="3">COD</td><td>36Mbps</td><td>0.0%</td><td>35Mbps</td><td>0.0%</td><td>36Mbps</td><td>0.0%</td><td>-0.8Mbps</td><td>0.0%</td><td>0.1Mbps</td><td>0.0%</td></tr><tr><td>25Mbps</td><td>31.0%</td><td>21Mbps</td><td>41.0%</td><td>22Mbps</td><td>39.2%</td><td>-4.1Mbps</td><td>+10.0%</td><td>-2.9Mbps</td><td>+8.2%</td></tr><tr><td>20Mbps</td><td>44.8%</td><td>16Mbps</td><td>53.3%</td><td>18Mbps</td><td>50.9%</td><td>-3.5Mbps</td><td>+8.5%</td><td>-2.1Mbps</td><td>+6.1%</td></tr><tr><td rowspan="3">DEVIL</td><td>31Mbps</td><td>0.0%</td><td>32Mbps</td><td>0.0%</td><td>32Mbps</td><td>0.0%</td><td>0.3Mbps</td><td>0.0%</td><td>0.2Mbps</td><td>0.0%</td></tr><tr><td>22Mbps</td><td>30.8%</td><td>20Mbps</td><td>37.8%</td><td>20Mbps</td><td>37.4%</td><td>-2.0Mbps</td><td>+7.0%</td><td>-1.9Mbps</td><td>+6.6%</td></tr><tr><td>17Mbps</td><td>44.5%</td><td>16Mbps</td><td>50.9%</td><td>16Mbps</td><td>50.7%</td><td>-1.8Mbps</td><td>+6.4%</td><td>-1.8Mbps</td><td>+6.2%</td></tr><tr><td rowspan="3">FORZA</td><td>41Mbps</td><td>0.0%</td><td>41Mbps</td><td>0.0%</td><td>41Mbps</td><td>0.0%</td><td>-0.3Mbps</td><td>0.0%</td><td>-0.6Mbps</td><td>0.0%</td></tr><tr><td>26Mbps</td><td>36.4%</td><td>22Mbps</td><td>46.1%</td><td>22Mbps</td><td>46.2%</td><td>-4.1Mbps</td><td>+9.7%</td><td>-4.3Mbps</td><td>+9.8%</td></tr><tr><td>20Mbps</td><td>52.4%</td><td>16Mbps</td><td>61.1%</td><td>16Mbps</td><td>61.5%</td><td>-3.7Mbps</td><td>+8.7%</td><td>-4.0Mbps</td><td>+9.1%</td></tr><tr><td rowspan="3">PES</td><td>38Mbps</td><td>0.0%</td><td>34Mbps</td><td>0.0%</td><td>35Mbps</td><td>0.0%</td><td>-3.4Mbps</td><td>0.0%</td><td>-3.3Mbps</td><td>0.0%</td></tr><tr><td>29Mbps</td><td>24.6%</td><td>21Mbps</td><td>38.2%</td><td>23Mbps</td><td>34.0%</td><td>-7.3Mbps</td><td>+13.6%</td><td>-5.7Mbps</td><td>+9.4%</td></tr><tr><td>24Mbps</td><td>38.0%</td><td>20Mbps</td><td>42.1%</td><td>19Mbps</td><td>43.8%</td><td>-3.5Mbps</td><td>+4.1%</td><td>-4.0Mbps</td><td>+5.8%</td></tr><tr><td rowspan="3">Average</td><td>37Mbps</td><td>0.0%</td><td>36Mbps</td><td>0.0%</td><td>36Mbps</td><td>0.0%</td><td>-1.0Mbps</td><td>0.0%</td><td>-0.9Mbps</td><td>0.0%</td></tr><tr><td>25Mbps</td><td>30.7%</td><td>21Mbps</td><td>40.8%</td><td>22Mbps</td><td>39.2%</td><td>-4.4Mbps</td><td>+10.1%</td><td>-3.7Mbps</td><td>+8.5%</td></tr><tr><td>20Mbps</td><td>44.9%</td><td>17Mbps</td><td>51.8%</td><td>17Mbps</td><td>51.7%</td><td>-3.1Mbps</td><td>+6.9%</td><td>-3.0Mbps</td><td>+6.8%</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>Fig. 5. MOS score distribution per game; colors indicate encoding scenarios, while markers distinguish the games.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 6. MOS RD performance, averaged over entire games.</figcaption></figure>

1) *Objective Assessment:* The Rate Distortion (RD) chart in Fig. 4 provides the following key observations:

* **Overall Performance:** SVC w. BitExt (server-side) delivers the highest perceived quality, while ROI SVC

(network-side) delivers the lowest, demonstrating the advantage of server-side bandwidth adaptation during stable phases; however, it is unavailable during the transient phase after a sudden bandwidth reduction event.

* **High vs Low Bitrates:** At high bitrates, ROI SVC achieves nearly the same quality as STD SVC and SVC w. BitExt; however, its performance degrades significantly under bandwidth constraints.

* **Network vs Server-side Processing:** Only STD SVC after packet wash (network-side) offers quality comparable to SVC w. BitExt (server-side); however, it is not washable below 20 Mbps (without re-encoding at a higher QP, which would add latency and violate fairness since all scenarios use the same coding configurations). In contrast, ROI SVC (network-side) still enables quality adaptation without outages, though with a higher quality drop.

* **VMAF vs LPIPS:** While both ROI SVC solutions deliver lower quality than STD SVC (with larger gaps in
VMAF than LPIPS), they achieve greater bitrate reduction through packet wash at the network edge.

*   **Benefit of ROI SVC(Ellip.&Map):** When comparing ROI SVC configurations, using a reduced elliptical zone with additional game-specific regions outperforms a single larger ellipse. Excluding the game map from gblur yields better visual quality, as distributing smaller blur regions across the frame (with the same total blur area) is more easily compensated by enh. layer 2.

2) *Subjective Assessment:* The following key observations can be drawn from Figs. 5, 6:

*   **Overall Performance:** SVC w. BitExt (server-side) delivers the highest perceived quality, while ROI SVC (network-side) delivers the lowest, again highlighting the advantage of server-side processing when possible.

*   **ROI Strategies:** ROI SVC(Ellip.&Map) achieves noticeably better visual quality than ROI SVC(Ellip.) (oranges achieve higher MOS than blues, see Figs. 5, 6), emphasizing the importance of the game-specific map to the players.

*   **Score Distribution:** The lowest scores occur for COD (1st person point of view) with ROI SVC(Ellip.), reflecting more noticeable decoding artifacts after packet wash, whereas this effect is less visible for other games.

The results highlight that, although ROI SVC(Ellip.&Map) can be washed nearly the same as SVC(Ellip.), it delivers higher visual quality meanwhile having a better adaptation capability than STD SVC. It is important to note that the network-side processing serves as a temporary (or emergency) solution to prevent user-side outages, independently of the server-side congestion control solution. When the sender reduces the bitrate (e.g., re-encoding by higher QP) or bandwidth recovers, no quality gap remains between server and network, eliminating the need for further packet wash.

## VI. CONCLUSION

This paper evaluates the effectiveness of various SVC encoding strategies in enabling the BPP packet wash to cope with unexpected bandwidth drops. Packet wash operates at the network edge, managing sudden congestion at line rate to ensure uninterrupted gameplay. While after applying packet wash, STD SVC maintains similar quality compared to traditional server-side processing, ROI SVC exhibits lower quality but with a greater packet wash capability. The superior performance of ROI SVC after packet wash is achieved through a trade-off in rate-quality for only non-critical regions, while key areas remain at high quality. Future work will compare packet wash with new network architectures such as L4S.

## ACKNOWLEDGMENT

This work was funded by the French ANR PEPR NFNAI project, No ANR-22-PEFT-0003.

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