To boldly go:an occam-π mission to engineer emergence

Future systems will be too complex to design and implement explicitly. Instead, we will have to learn to engineer complex behaviours indirectly: through the discovery and application of local rules of behaviour, applied to simple process components, from which desired behaviours predictably emerge through dynamic interactions between massive numbers of instances. This paper describes a process-oriented architecture for fine-grained concurrent systems that enables experiments with such indirect engineering. Examples are presented showing the differing complex behaviours that can arise from minor (non-linear) adjustments to low-level parameters, the difficulties in suppressing the emergence of unwanted (bad) behaviour, the unexpected relationships between apparently unrelated physical phenomena (shown up by their separate emergence from the same primordial process swamp) and the ability to explore and engineer completely new physics (such as force fields) by their emergence from low-level process interactions whose mechanisms can only be imagined, but not built, at the current time

Coffee: Cost-Effective Edge Caching for 360 Degree Live Video Streaming

While live 360 degree video streaming delivers immersive viewing experience, it poses significant bandwidth and latency challenges for content delivery networks. Edge servers are expected to play an important role in facilitating live streaming of 360 degree videos. In this paper, we propose a novel predictive edge caching algorithm (Coffee) for live 360 degree video that employ collaborative FoV prediction and predictive tile prefetching to reduce bandwidth consumption, streaming cost and improve the streaming quality and robustness. Our light-weight caching algorithms exploit the unique tile consumption patterns of live 360 degree video streaming to achieve high tile caching gains. Through extensive experiments driven by real 360 degree video streaming traces, we demonstrate that edge caching algorithms specifically designed for live 360 degree video streaming can achieve high streaming cost savings with small edge cache space consumption. Coffee, guided by viewer FoV predictions, significantly reduces back-haul traffic up to 76% compared to state-of-the-art edge caching algorithms. Furthermore, we develop a transcoding-aware variant (TransCoffee) and evaluate it using comprehensive experiments, which demonstrate that TransCoffee can achieve 63\% lower cost compared to state-of-the-art transcoding-aware approaches

EFFECT ON 360 DEGREE VIDEO STREAMING WITH CACHING AND WITHOUT CACHING

People all around the world are becoming more and more accustomed to watching 360-degree videos, which offer a way to experience virtual reality. While watching videos, it enables users to view video scenes from any perspective. To reduce bandwidth costs and provide the video with less latency, 360-degree video caching at the edge server may be a smart option. A hypothetical 360-degree video streaming system can partition popular video materials into tiles that are cached at the edge server. This study uses the Least Recently Used (LRU) and Least Frequently Used (LFU) algorithms to accomplish video caching and suggest a system architecture for 360-degree video caching. Two 360-degree videos from 48 users\u27 head movements are used in the experiment, and caching between the LRU cache and LFU cache is compared by changing the cache size. The findings demonstrate that, for varied cache sizes, utilizing LFU caching outperforms LRU caching in terms of average cache hit rate. In the first part of the research, we compared LRU and LFU caching algorithm. In the second part of the research, a suitable caching strategy model was developed based on user’s field of view. Field of view (FoV) is the term used to describe the portion of the 3600 videos that viewers typically see when watching 3600 videos. Edge caching can be a smart way to increase customer satisfaction while maximizing bandwidth usage (QoE). A 3600-video caching strategy has been developed in this study using three machine learning models that use random forest, linear regression, and Bayesian regression. As features, tiles\u27 frequency, user\u27s view prediction probability, and resolution were used. The created machine learning models are designed to decide the caching method for 360-degree video tiles. The models can forecast the frequency of viewing for 3600 video tiles (subsets of a full video). With a predictive R2 value of 0.79, the random forest regression model performs better than the other suggested models when the outcomes of the three developed models are compared. In the third part of the research, to compare our machine learning algorithm with LRU algorithm, a python test bench program was written to evaluate both algorithms on the test set by varying the cache size. The results demonstrate that our machine learning approach, which was created for 360-degree video caching, outperforms the LRU algorithm

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Tile-based edge caching for 360° live video streaming

360° video is becoming an increasingly popular technology on commercial social platforms and vital part of emerging Virtual Reality/Augmented Reality (VR/AR) applications. However, the delivery of 360° video content in mobile networks is challenging because of its size. The encoding of 360° video into multiple quality layers and tiles and edge cache-assisted video delivery have been proposed as a remedy to the excess bandwidth requirements of 360° video delivery systems. Existing works using the above tools have shown promising performance for Video-on-Demand (VoD) 360° delivery, but they cannot be straightforwardly extended in a live-streaming setup. Motivated by the above, we study edge cache-assisted 360° live video streaming to increase the overall quality of the delivered 360° videos to users and reduce the service cost. We employ Long Short-Term Memory (LSTM) networks to forecast the evolution of the content requests and prefetch content to caches. To further enhance the delivered video quality, users located in the overlap of the coverage areas of multiple Small Base Stations (SBSs) are allowed to receive data from any of these SBSs. We evaluate and compare the performance of our algorithm with Least Frequently Used (LFU), Least Recently Used (LRU), and First In First Out (FIFO) algorithms. The results show the superiority of the proposed approach in terms of delivered video quality, cache-hit-ratio and backhaul link usage

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Interactive Food and Beverage Marketing: Targeting Children and Youth in the Digital Age

Looks at the practices of food and beverage industry marketers in reaching youth via digital videos, cell phones, interactive games and social networking sites. Recommends imposing governmental regulations on marketing to children and adolescents