DASH-based video transmission over LTE networks

The transmission control protocol (TCP) is one of the most important Internet protocols. It provides reliable transport services between two end-hosts. Since TCP performance affects overall network performance, many studies have been done to model TCP performance in the steady state. However, recent researches have shown that most TCP flows are short-lived. Therefore, it is more meaningful to model TCP performance in relation to the initial stage of short-lived flows. In addition, the next-generation Internet will be an unified all-IP network that includes both wireless and wired networks integrated together. In short, modelling short-lived TCP flows in wireless networks constitutes an important axis of research. In this paper, we propose simple wireless TCP models for short-lived flows that extend the existing analytical model proposed in [IEEE Commun. Lett. 2002; 6(2):85–88]. In terms of wireless TCP, we categorized wireless TCP schemes into three types: end-to-end scheme, split connection scheme, and local retransmission scheme, which is similar to the classification proposed in [IEEE/ACM Trans. Networking 1997; 756–769]. To validate the proposed models, we performed ns-2 simulations. The average differences between the session completion time calculated using the proposed model and the simulation result for three schemes are less than 9, 16, and 7 ms, respectively. Consequently, the proposed model provides a satisfactory means of modelling the TCP performance of short-lived wireless TCP flows. Copyright © 2005 John Wiley & Sons, Ltd.

Performance evaluation of wireless TCP with rerouting in mobile networks

Over the past decade, video streaming on the Internet has become the primary source of our media consumption. Billions of users stream online video on multiple devices with an increasing expectation that video will be delivered at high quality without any rebuffering or other events that affect their Quality of Experience (QoE). Video streaming platforms leverage Content Delivery Networks (CDNs) to achieve this at scale. However, there is a gap in how the quality of video streams is monitored. Current solutions rely on client-side beacons that are issued actively by video players. While such approaches may be feasible for streaming platforms that deploy their own CDN, they are less applicable for third-party CDNs with multiple tenants and diverse video players.In this paper, we present a characterization of video workload from a global multi-tenant CDN and develop SSQoE: a methodology deployed on the server side which estimates rebuffering experienced by video clients using passive measurements. Using this approach, we calculate a QoE score which represents the health of a video stream across multiple consumers. We present our findings using this QoE score for various scenarios and compare it to traditional server and network monitoring metrics. We also demonstrate the QoE score’s efficacy during large streaming events such as the 2020 Superbowl LIV. We show that this server-side QoE estimation methodology is able to track video performance at an AS or user agent level and can easily pinpoint regional issues at the CDN, making it an attractive solution to be explored by researchers and other CDNs.

Adaptive psychometric scaling for video quality assessment

The proliferation of online video consumption, particularly for live events attracting massive global audiences, necessitates robust and scalable content delivery mechanisms. This article explores the critical role of Content Delivery Networks (CDNs) in facilitating the seamless distribution of live video streams to millions of concurrent viewers. It explores the fundamental architecture of CDNs, including edge servers, routing methodologies such as Geographic Load Balancing and Anycast Routing, and the importance of caching and content replication. Furthermore, it analyzes how these technologies are specifically applied to the delivery of high-demand live events, using the "Big Game" as a salient example. The "Big Game" refers to the major annual championship football event in the United States that consistently ranks among the most-watched broadcasts worldwide. The document concludes by considering emerging trends in CDN technology that promise to further enhance the efficiency and performance of live video delivery.

This paper proposes a novel video delivery scheme that reduces the bandwidth consumption cost from a video server to terminals in Long-Term Evolution networks. This proposed scheme combines optimized hybrid multicast with a segment-based caching strategy for use in environments where the maximum number of multicast channels is limited. The optimized hybrid multicast, allocation of multicast channels, and cache allocation are determined on the basis of a video's request rate, the related video's length, and the variable cost per unit size of a segment belonging to the related video. Performance evaluation results show that the proposed scheme reduces a video's delivery costs. This work is applicable to on-demand TV services that feature asynchronous video content requests.

In this paper we propose a novel segmentation-based approach for delivering multimedia files in content distribution networks. Each media file is divided into segments and the number of copies distributed to replica servers for each segment is designed in such a way that we can reduce the storage requirement at the replica servers, accommodate the bursty arrival of client requests, and at the same time achieve a small latency for the clients. We analyze the effect of the size of segments on the total storage requirement and give a lower bound. We design a scheduling algorithm that can take advantage of the increased capacity of access links of clients. Our simulations show that the segmentation-based approach can reduce the latency and the rejection rate significantly over those approaches that deliver the whole or the prefix part of media files to replica servers.

P2MCMD: A scalable approach to VoD service over peer-to-peer networks

Transmission distortion estimation for real-time video delivery over hybrid channels with bit errors and packet erasures

With the proliferation of vehicle technologies to support sophisticated features like assisted and autonomous driving, advanced communication protocols like cellular-vehicle-to-everything (CV2X) have been proposed. However, practical large-scale deployments have been hindered due to caveats such as hardware, security, and cellular infrastructure demands. This work presents and evaluates a practical approach to utilizing ARM TrustZone to turn commercial off-the-shelf smartphones into secure CV2X radios that communicate over the LTE network. These smartphone-based CV2x radios communicate with each other via an intermediary server placed outside/within the LTE infrastructure without affecting normal operations of the phone, like using navigation, calls, and music. Vehicle owners would only have to install the CV2X application to use their smartphones as CV2X radios. The approach would boost the adoption of CV2X by reducing the requirement for dedicated hardware and reusing existing infrastructure. In this work, we empirically evaluate the on-device overhead coupled with various network topologies concerning the location of an intermediary server and the LTE infrastructure. We show that our proposed approach can meet the required real-time constraints for safe CV2X operation while ensuring the integrity of the on-device communication from manipulation by remote attackers.KeywordsCV2XCOTS devicesLTESecure communicationTEETrustzone

Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be laborintensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we create the Intelligent Automated Client Diagnostic (IACD) system, which only relies on collection of Transmission Control Protocol (TCP) packet traces. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems and (ii) identifies characteristics unique to the specific fault to report the root cause. The modular design of the system enables support for new access link and fault types. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy. The system can perform fault diagnosis independent of the user's specific TCP implementation, enabling diagnosis of diverse range of client devices

Energy-saving for the LTE-A network with relay nodes in TDD mode is addressed in this paper, and integrated sleep scheduling schemes for relay nodes and user devices under a base station are designed. The authors’ two previously proposed ideas, namely, Load-Based Power Saving (LBPS) and Virtual Time, are adopted in the design, and two strategies, namely, top-down and bottom-up each with three LBPS schemes are proposed. In the top-down strategy, the load as well as the channel quality on the backhaul link is first considered to determine the sleep pattern for all relay nodes, and then the sleep schedule for UEs under each relay node is determined accordingly. On the contrary, the load and the channel quality on the access links are first considered and then integrated into the sleep schedule on the backhaul link. Two associated mechanisms for the proposed LBPS schemes to operate in the virtual time domain are also proposed in the paper, i.e., calculation of the virtual subframe capacity and the mapping mechanisms from the virtual time to the actual time. The benefit of the proposed schemes in power saving over the standard-based scheme is demonstrated by the simulation study, and the bottom-up scheme of BU-Split outperforms the other schemes under equally distributed input load as well as the hotspot scenario. Discussion on the tradeoff of the processing overhead and the performance for the proposed schemes is presented in the paper.

Multimedia-related businesses dealing with movie streaming and video-based short messages have increased the global Internet video traffic substantially in the last couple of years. At the same time, multimedia on the Web has been standardized in terms of codecs and browser-based JavaScript APIs. However, the technological challenges concerning the distribution of large video files are today mainly tackled by scaling up capacities in cloud data centers, or relying on content delivery networks. Both approaches favor financially strong, large companies, while independent video providers with highly demanded videos are disadvantaged. In this article, we conceptualize methods to offload video delivery from centralized clouds to clients. In particular, we implemented and evaluated a system that streams videos peer-to-peer via WebTorrent in HTML5. The resulting library is available open source.

Over the past few years video viewership over the Internet has risen dramatically and market predictions suggest that video will account for more than 50% of the traffic over the Internet in the next few years. Unfortunately, there has been signs that the Content Delivery Network (CDN) infrastructure is being stressed with the increasing video viewership load. Our goal in this paper is to provide a first step towards a principled understanding of how the content delivery infrastructure must be designed and provisioned to handle the increasing workload by analyzing video viewing behaviors and patterns in the wild. We analyze various viewing behaviors using a dataset consisting of over 30 million video sessions spanning two months of viewership from two large Internet video providers. In these preliminary results, we observe viewing patterns that have significant impact on the design of the video delivery infrastructure.

We consider the problem of optimizing video delivery for a network supporting video clients streaming stored video. Specifically, we consider the joint optimization of network resource allocation and video quality adaptation. Our objective is to fairly maximize video clients' Quality of Experience (QoE) realizing tradeoffs among the mean quality, temporal variability in quality, and fairness, incorporating user preferences on rebuffering and cost of video delivery. We present a simple asymptotically optimal online algorithm, NOVA, to solve the problem. NOVA is asynchronous, and using minimal communication, distributes the tasks of resource allocation to network controller, and quality adaptation to respective video clients. Video quality adaptation in NOVA is also optimal for standalone video clients, and is well suited for use in the DASH framework. Further, NOVA can be extended for use with more general QoE models, networks shared with other traffic loads and networks using fixed/legacy resource allocation.