Edge-Assisted Short Video Sharing with Guaranteed Quality-of-Experience

Abstract : As a rising star of social apps, short video apps, e.g., TikTok, have attracted a large number of mobile users by providing fresh and short video contents that highly match their watching preferences. Meanwhile, the booming growth of short video apps imposes new technical challenges on the existing computation and communication infrastructure. Traditional solutions maintain all videos on the cloud and stream them to users via contend delivery networks or the Internet. However, they incur huge network traffic and long delay that seriously affect users' watching experiences. In this paper, we propose an edge-assisted short video sharing framework to address these challenges by caching some videos highly preferred by users at edge servers that can be accessed by users via high-speed network connections. Since edge servers have limited computation and storage resources, we design an online algorithm with provable approximation ratio to decide which videos should be cached at edge servers, without the knowledge of future network quality and watching preferences changes. Furthermore, we improve the performance by jointly considering video fetching and user-edge association. Extensive simulations are conducted to evaluate the proposed algorithms under various system settings, and the results show that our proposals outperform existing schemes.

 EXISTING SYSTEM :

 ? Access histories to large VoD services are not public for obvious privacy reasons, and it would be unethical to exploit the lack of privacy of existing services to collect such data. ? However, the processing of the request is performed inside an SGX enclave and the attacker may only learn the co-existence of the request and flows to video servers and assisting peers, and not determine which precise video was requested, and if it was a legitimate or fake request. ? Supporting the VoD model requires being able to quickly discover a specific video in a catalog, and ensure that copies of each video exist in the system at all times, making it less amenable to a fully decentralized implementation.

 DISADVANTAGE :

 ? We survey solution techniques that solve the resource allocation problem of MEC-based video streaming. ? Different from traditional networks, MEC has many significant advantages, which can effectively solve the problems of high delay and low efficiency in traditional networks. ? With the distributed-based approach, each node/user can make the decision individually, and the optimization problem can be divided into some subproblems. ? Several distributed approaches are adopted to solve the resource optimization problem, such as the alternating direction method of the multipliers (ADMM), multiuser-based game model (MGM), and blockchain-based approaches.

 PROPOSED SYSTEM :

 • This is also not compatible with VoD bandwidth requirements. Some video streaming services with privacy as a design goal have been proposed. • All these solutions target fully peerto-peer approaches, without core servers. • Several authors proposed to complement a limited set of servers with edge peer resources as we do in PrivaTube. • Push-to-Peer targets a deployment on controlled networks, including for instance set-topboxes deployed by an ISP. • Previous approaches have proposed to add noise to legitimate traffic in order to conceal the interests of users, as fake requests allow in PrivaTube.

 ADVANTAGE :

 ? In terms of video transmission, the content delivery network (CDN) is adopted to distribute in cloudmanner content, enhancing media availability and distribution performance. ? Macro-cellular networks performance can be increased by increasing the efficiency of the transceiver. ? In the wireless cellular networks, the joint framework considering computation offloading, spectrum resource allocation, and content caching are proposed to improve the performance of video streaming with MEC. ? The performance of a on/off switching strategy can be evaluated by implementing the proposed algorithm into a real network. ? Moreover, a novel algorithm is designed to group players into clusters to optimize the performance.