Extended Reality (XR) - which includes Virtual Reality and Augmented Reality - promises to bring the virtual and telepresence experience to another level. Unfortunately, solutions leveraging these technologies require special high-performance computing platforms that degrade the cost-benefit balance. Moving processing to the cloud solves this problem but imposes strict requirements on data transmission reliability, bandwidth, and delays. The satisfaction of these requirements becomes an extremely challenging problem in the presence of other types of delay-sensitive traffic, such as remote control, industrial automation, or the control commands of the Cloud XR application itself. This article studies the joint service of the adaptive Cloud XR traffic with other high-priority delay-sensitive traffics. The paper develops an analytical model of the considered communication system. The model represents the system as a discrete state Markov chain and estimates the quality of experience for Cloud XR users in various scenarios. Using the model, the paper estimates the network capacity for the Cloud XR traffic and optimizes the bitrate adaptation function of the Cloud XR video streaming application. The goal of the optimization is to improve the visual quality of the virtual environment observed by the users, subject to the constrained probability of image impairments due to excessive delivery delays. Numerical results demonstrate the high accuracy of the developed model and the benefits provided by the optimization.

Industrial Internet of Things applications require low and deterministic delays, which is a challenging target for emerging wireless communication technologies. While the upcoming Wi-Fi 7 and Wi-Fi 8, tailored for real-time and reliable communications, are under development, this article focuses on Wi-Fi 6 and explores its capabilities to support industrial applications. This article summarizes key lessons learned during an industrial project on factory automation with Wi-Fi 6, describes and advocates a solution that satisfies quality of service requirements in industrial scenarios. To generalize the designed scheme, this article proposes a framework that maximizes the capacity of the system under reliability and latency constraints. Through extensive simulations, this article shows that the proposed scheme more than doubles the network capacity compared with the default Wi-Fi 6 implementation in terms of the number of stations served with low and deterministic packet delivery delays.

Although the widely-used Transport Layer Security (TLS) protocol hides application data, an unencrypted part of the TLS handshake, specifically the server name indication (SNI), is a backdoor for encrypted traffic classification frameworks. The recently developed Encrypted ClientHello (ECH) amendment to the TLS protocol aims to protect the privacy-sensitive content of the ClientHello message, including SNI. Conversely, ECH can be a game-changer in the early detection of encrypted traffic. The paper shows that the performance of the state-of-the-art traffic classification algorithms degrades significantly with the introduction of the ECH. Hence, novel approaches to real-time traffic classification are required. The paper develops two novel traffic classification algorithms to address this challenge. The first one uses unencrypted bytes of the TLS Hello messages as independent features of the Random Forest algorithm. It is extremely lightweight and suits throughput-focused traffic classification. It is faster than state-of-the-art algorithms by three times and achieves higher classification quality. The second algorithm augments the approach of the first one by focusing on the particular metadata of the handshake. This way, it efficiently extracts data from the exchange and achieves the highest classification quality in all the considered scenarios. It has a three times lower error rate than state-of-the-art algorithms and provides a reliable classification of ECH traffic.

Satisfying heterogeneous Quality of Service requirements of different applications in 5G systems requires tight interaction between the network and the applications. Although being pushed to 3GPP, SAND, the most mature solution enabling such interaction and cross-layer optimization for video streaming, has many limitations and drawbacks. This paper fills the gaps found in the SAND specification and enriches its applicability, by introducing a new general platform called xStream. xStream enables communication between various applications and the 5G system and opens the door for many innovative solutions for various types of traffic as well as optimal resource slicing (not only video ones). With extensive simulation, it is shown that xStream provides benefits for both OTT service providers and network operators significantly improving QoE for their customers.

Dynamic Adaptive Streaming over HTTP (DASH) is a predominant technology for delivering video in wired and wireless networks. With DASH, clients can adaptively select the quality (bitrate) of the downloaded video. To improve Quality of Experience (QoE), a recently standardized framework, called Server and Network Assisted DASH (SAND), allows the client and the server to exchange control information with intermediate network nodes, e.g., with Wi-Fi Access Point (AP). In the paper, we show that such a framework is especially fruitful for short videos, which becomes more and more important nowadays. Specifically, we design a novel centralized algorithm, called SEBRA, which runs at the AP. It both allocates video bitrates and distributes channel time among all video clients. With simulations, we show that the designed algorithm significantly improves QoE compared to the existing solutions.

Quality of service provisioning in modern networks requires traffic to be classified as quickly as possible according to its requirements and service type. However, traffic classification (TC) becomes increasingly challenging as traffic encryption evolves. The Encrypted ClientHello (ECH) amendment to the most widespread encryption protocol, Transport Layer Security (TLS), conceals the most sensitive metadata of the TLS-encrypted flows including the Server Name Indication (SNI), which provides ground-truth early TC. Nevertheless, the backward compatibility and protocol limitations leave some non-random TLS metadata open. This paper designs a new early traffic classifier called hybrid Random Forest Traffic Classifier (hRFTC) that utilizes unencrypted TLS metadata together with the statistical features of the traffic flows extracted before the arrival of any application data from the server side. The paper collects an up-to-date diversified traffic dataset in various countries of North America, Europe, and Asia, which is available online and is one of the largest, most detailed, and diversified open-source TC datasets. The paper evaluates the performance of the state-of-the-art TC algorithms on the collected dataset. The results reveal that unencrypted in ECH scenario TLS settings are similar for many multimedia services. Consequently, the TC algorithms that rely solely on the TLS features achieve as low as 38.4% classification F-score. Meanwhile, the hybrid approach of the hRFTC dramatically enhances the TC efficacy. hRFTC achieves up to a 94.6% F-score on the collected dataset, which is superior to the best state-of-the-art algorithms.