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Video over wireless
This practical volume offers expert guidance on delivering high-quality video communications over wireless networks. Wireless access has become the dominant medium for network connectivity due to the proliferation of smartphones and tablets. Because of the size and power limitations of these personal devices, as well as data caps imposed by cellular operators, there are significant constraints on the delivery of high-quality video content. This guide explains emerging technologies that will help overcome these challenges-all presented in a clear and concise format.
Book
Viewport prediction with cross modal multiscale transformer for 360° video streaming
In the realm of immersive video technologies, efficient 360° video streaming remains a challenge due to the high bandwidth requirements and the dynamic nature of user viewports. Most existing approaches neglect the dependencies between different modalities, and personal preferences are rarely considered. These limitations lead to inconsistent prediction performance. Here, we present a novel viewport prediction model leveraging a Cross Modal Multiscale Transformer (CMMST) that integrates user trajectory and video saliency features across different scales. Our approach outperforms baseline methods, maintaining high precision even with extended prediction intervals. By harnessing the Cross Modal attention mechanisms, CMMST captures intricate user preferences and viewing patterns, offering a promising solution for adaptive streaming in virtual reality and other immersive platforms. The code of this work is available at
https://github.com/bbgua85776540/CMMST
.
Journal Article
On Demand Secure Scalable Video Streaming for Both Human and Machine Applications
Scalable video coding plays an essential role in supporting heterogeneous devices, network conditions, and application requirements in modern video streaming systems. However, most existing scalable coding approaches primarily optimize human perceptual quality and provide limited support for data privacy, as well as for machine analyses and the integration of heterogeneous sensor data. This limitation motivated the development of adaptive scalable video coding frameworks. The proposed approach is designed to serve both human viewers and automated analysis systems while ensuring high security and compression efficiency. The method adaptively encrypts selected layers during transmission to protect sensitive content without degrading decoding or analysis performance. Experimental evaluations on benchmark datasets demonstrate that the proposed framework achieves superior rate distortion efficiency and reconstruction quality, while also improving machine analysis accuracy compared to existing traditional and learning-based codes. In video surveillance scenarios, where the base layer is preserved for analysis, the proposed scalable human machine coding (SHMC) method outperforms scalable extensions of H.265/High Efficiency Video Coding (HEVC), Scalable High Efficiency Video Coding (SHVC), reducing the average bit-per-pixel (bpp) by 26.38%, 30.76%, and 60.29% at equivalent mean Average Precision (mAP), Peak Signal-to-Noise Ratio (PSNR), and Multi-Scale Structural Similarity (MS-SSIM) levels. These results confirm the effectiveness of integrating scalable video coding with intelligent encryption for secure and efficient video transmission.
Journal Article
Remotely : travels in the binge of TV
A leading film critic discusses the evolving world of streaming media and its impact on society.
Book
A machine learning approach to classifying YouTube QoE based on encrypted network traffic
Due to the widespread use of encryption in Over-The-Top video streaming traffic, network operators generally lack insight into application-level quality indicators (e.g., video quality levels, buffer underruns, stalling duration). They are thus faced with the challenge of finding solutions for monitoring service performance and estimating customer Quality of Experience (QoE) degradations based solely on passive monitoring solutions deployed within their network. We address this challenge by considering the concrete case of YouTube, whereby we present a methodology for the classification of end users’ QoE when watching YouTube videos, based only on statistical properties of encrypted network traffic. We have developed a system called YouQ which includes tools for monitoring and analysis of application-level quality indicators and corresponding traffic traces. Collected data is then used for the development of machine learning models for QoE classification based on computed traffic features per video session. To test the YouQ system and methodology, we collected a dataset corresponding to 1060 different YouTube videos streamed across 39 different bandwidth scenarios, and tested various classification models. Classification accuracy was found to be up to 84% when using three QoE classes (“low”, “medium” or “high”) and up to 91% when using binary classification (classes “low” and “high”). To improve the models in the future, we discuss why and when prediction errors occur. Moreover, we have analysed YouTube’s adaptation algorithm, thus providing valuable insight into the logic behind the quality level selection strategy, which may also be of interest in improving future QoE estimation algorithms.
Journal Article
Netflix and the re-invention of television
\"This book deals with the various ways Netflix reconceptualises television as part of the process of TV IV. As television continues to undergo a myriad of significant changes, Netflix has proven itself to be the dominant force in this development, simultaneously driving a number of these changes and challenging television's existing institutional structures. This comprehensive study explores the pre-history of Netflix, the role of binge-watching in its organisation and marketing, and Netflix's position as a transnational broadcaster. It also examines different concepts of control and the role these play in the history of ancillary technologies, from the remote control to binge-watching as Netflix's iteration of giving control to the viewers. By focusing on Netflix's relationship with the linear television schedule, its negotiations of quality and marketing, as well as the way Netflix integrates into national media systems, Netflix and the Re-invention of Television illuminates the importance of Netflix's role within the processes of TV IV.\"-- Provided by publisher.
Book
Live multicast video streaming from drones: an experimental study
We present and evaluate a multicast framework for point-to-multipoint and multipoint-to-point-to-multipoint video streaming that is applicable if both source and receiver nodes are mobile. Receiver nodes can join a multicast group by selecting a particular video stream and are dynamically elected as designated nodes based on their signal quality to provide feedback about packet reception. We evaluate the proposed application-layer rate-adaptive multicast video streaming over an aerial ad-hoc network that uses IEEE 802.11, a desirable protocol that, however, does not support a reliable multicast mechanism due to its inability to provide feedback from the receivers. Our rate-adaptive approach outperforms legacy multicast in terms of goodput, delay, and packet loss. Moreover, we obtain a gain in video quality (PSNR) of 30% for point-to-multipoint and of 20% for multipoint-to-point-to-multipoint streaming.
Journal Article
Netflixھ : how Reed Hastings changed the way we watch movies & TV
Netflix was once only an idea in the mind of Reed Hastings, a businessman who has done amazing things since starting the online movie and TV company. Discover how Reed was able to make Netflix a success around the world and find out what he has planned next to keep the company on top.
BOOK
SABA: segment and buffer aware rate adaptation algorithm for streaming over HTTP
Adaptive streaming allows for dynamic adaptation of the bitrate to varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput, as the throughput varies widely over time. The current throughput estimation methods cannot distinguish between throughput fluctuations of different amplitude and frequency. In this paper, we propose a throughput estimation method that accurately estimates the throughput based on previous throughput samples. It is robust to short term and small fluctuations, and sensitive to large fluctuations in throughput. Furthermore, we propose a rate adaptive algorithm for video on demand (VoD) services that selects the quality of the video based on the estimated throughput and playback buffer occupancy. The objective of the rate adaptive algorithms is to guarantee high video quality to improve user experience. The proposed algorithm dynamically adjusts the quality level of the video stream. The proposed method selects high quality video segments, while minimizing the risk of playback interruption. Furthermore, the proposed method minimizes the frequency of video rate changes. We show that the algorithm smoothly switches the video rate to improve user experience. Furthermore, we determine that it efficiently utilizes network resources to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that variations in the playback buffer size and segment duration do not affect the performance of the proposed algorithm.
Journal Article