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1. Navigation Graph for Tiled Media Streaming

   https://doi.org/10.1145/3343031.3351021  

   Park, Jounsup; Nahrstedt, Klara (October 2019, ACM International Conference on Multimedia)

   After the emergence of video streaming services, more creative and diverse multimedia content has become available, and now the capability of streaming 360-degree videos will open a new era of multimedia experiences. However, streaming these videos requires larger bandwidth and less latency than what is found in conventional video streaming systems. Rate adaptation of tiled videos and view prediction techniques are used to solve this problem. In this paper, we introduce the Navigation Graph, which models viewing behaviors in the temporal (segments) and the spatial (tiles) domains to perform the rate adaptation of tiled media associated with the view prediction. The Navigation Graph allows clients to perform view prediction more easily by sharing the viewing model in the same way in which media description information is shared in DASH. It is also useful for encoding the trajectory information in the media description file, which could also allow for more efficient navigation of 360-degree videos. This paper provides information about the creation of the Navigation Graph and its uses. The performance evaluation shows that the Navigation Graph based view prediction and rate adaptation outperform other existing tiled media streaming solutions. Navigation Graph is not limited to 360-degree video streaming applications, but it can also be applied to other tiled media streaming systems, such as volumetric media streaming for augmented reality applications. 

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2. Automated, Cross-Layer Root Cause Analysis of 5G Video-Conferencing Quality Degradation

   Yi, Fan; Wan, Haoran; Jamieson, Kyle; Michel, Oliver (October 2025, ACM Internet Measurement Conference (IMC))

   5G wireless networks leverage complex scheduling, retransmission, and adaptation mechanisms to maximize their efficiency. These mechanisms interact to produce significant fluctuations in uplink and downlink capacity and latency, markedly impacting the the performance of real-time communication and multimedia applications, such as video conferencing. These applications are particularly sensitive to such fluctuations, resulting in lag, stuttering, distorted audio, and low video quality. In this paper, we present a cross-layer view of 5G networks and their impact on and interaction with video-conferencing applications. We conduct novel, detailed measurements of both private CBRS and commercial carrier cellular network dynamics, capturing physical- and link-layer events and correlating them with their effects at the network and transport layers, and the video-conferencing application itself. Our two datasets comprise days of low-rate campus-wide Zoom telemetry data, and hours of high-rate, correlated WebRTC-network-5G telemetry data. Based on these data, we trace performance anomalies back to root causes, identifying 24 previously unknown causal event chains that degrade 5G video conferencing. Armed with this knowledge, we build Domino, a tool that automates this process and is user-extensible to future wireless networks and interactive applications. 

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3. Deep learning approach to multimedia traffic classification based on QoS characteristics

   https://doi.org/10.1049/iet-net.2018.5179  

   Wang, Zaijian; Mao, Shiwen; Yang, Weidong (May 2019, IET Networks)

   With the fast increase of multimedia traffic in Internet of Things (IoT) applications, IoT traffic now requires very different Quality of Service (QoS). By extensive statistical analysis of traffic flow data from a real world network, the authors find that there are some latent features hidden in the multimedia data, which can be useful for accurately differentiating multimedia traffic flows from the QoS perspective. Under limited training data conditions, existing shallow classification methods are limited in performance, and are thus not effective in classifying emerging multimedia traffic types, which have truly entered the era of big data and become very completed in QoS features. This situation inspires us to revisit the multimedia traffic classification problem with a deep learning (DL) approach. In this study, an improved DL‐based multimedia traffic classification method is proposed, which considers the inherent structure of QoS features in multimedia data. An improved stacked autoencoder model is employed to learn the relevant QoS features of multimedia traffic. Extensive experimental studies with multimedia datasets captured from a campus network demonstrate the effectiveness of the proposed method over six benchmark schemes. 

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4. ASVTuw: Adaptive Scalable Video Transmission in Underwater Acoustic Multicast Networks

   https://doi.org/10.1145/3567600.3568137  

   Qi, Zhuoran; Petroccia, Roberto; Pompili, Dario (November 2022, WUWNet'22: The 16th International Conference on Underwater Networks & Systems)

   Scalable Video Coding (SVC) has been widely used in video transmissions. However, inappropriate SVC structures may lead to received video quality lower than user’s requirement or resource waste, especially in underwater time-varying channels. In this work, an adaptive cross-layering solution is proposed and validated for video transmissions in underwater acoustic multicast networks, namely Adaptive Scalable Video Transmission (ASVTuw). In ASVTuw, the transmitter collects over time the information about the channel states and the users’ video quality requirements to adaptively select the SVC video structures and transmission schemes, using Machine Learning (ML). At-sea experiments were conducted to collect the required acoustic data. The collected data were then used in MATLAB simulations to validate the ASVTuw. The results show that the usage of ASVTuw avoids resource wasting from transmitting redundant SVC substreams and satisfies the multicast users’ video quality requirements effectively with higher flexibility compared with the existing noncross-layering designs. 

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5. Advances in Reliable File-Stream Multicasting over Multi-Domain Software Defined Networks (SDN)

   https://doi.org/10.1109/ICCCN.2019.8847110  

   Tan, Yuanlong; Chen, Shuoshuo; Emmerson, Steve; Zhang, Yizhe; Veeraraghavan, Malathi (September 2019, 2019 28th International Conference on Computer Communication and Networks (ICCCN))

   In prior work, we proposed a cross-layer architecture called Multicast-Push Unicast-Pull (MPUP) for Software Defined Networks (SDN) to support a reliable file-stream multicast application. In this work, we improved the algorithms used to set parameters: transport-layer sender retransmission timer, VLAN rate (which is also the sending rate) and sender-buffer size. Experimental evaluation using feeds with metadata collected from real meteorology file streams was conducted. A significant finding is that the throughput achieved is smaller than the VLAN/sending rate even though file blocks are multicast continuously in UDP datagrams. Sender-buffer waiting times and propagation delays are the main reasons for the degraded throughput. For example, increasing the VLAN rate from 20 Mbps to 500 Mbps, reduced the degradation from 90% to 45%. However, the degradation increased from 45% to 58% when the VLAN rate was increased from 500 Mbps to 1 Gbps. We found an increase in the number of block retransmissions at the higher rates, which explains this increased degradation. Increasing RTT from 0.1 ms to 100 ms caused throughput to drop from 274.8 Mbps to 27.6 Mbps on a 500 Mbps VLAN. If transmission delay was a significant component in total latency, then throughput degradation relative to VLAN rate would be small; however, the meteorology file-streams used in our study have small-sized data products. Due to bandwidth borrowing between VLAN and IP-routed services, VLAN utilization is not important, and hence we recommend using the smallest rate at which sender-buffer waiting times are insignificant. 

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