Large-Scale Delivery of Time-shifted Streams

Time-shifted streaming is a general term, which covers two main services: catch-up TV and life-streaming.

In catch-up TV, a program normally broadcast at time t can be viewed at any time after t (from a few seconds to many days). Catch-up TV is provided through network digital video recorders or personal video recorders. Catch-up TV is gaining in popularity: it accounts now for 14% of the overall TV consumption in UK households equipped with DVRs. It is also the TV usage that grew at the highest rate in 2009 in the US. However, despite the efforts of many companies, including the French SME Anevia, catch-up streaming services are still expensive to deploy because conventional disk-based VoD servers cannot massively ingest content, and keep pace with the changing viewing habits of subscribers. Moreover, clients require distinct portions of a stream so no group communication techniques such as peer-to-peer and multicast protocols can be used. Therefore, only big media actors and TV incumbents can offer these services at large scale, which is bad for innovation. Here, a peer-assisted architecture could help start-up and non-profit associations to also propose time-shifted streams to their users. 

In parallel, a new form of streams is emerging: life-streams. The concept originally coined by Vannevar Bush is currently revisited by popular social network tools like twitter: every user is a producer of a life-stream, a stream of personal data that is inherently made public in order to be consumed by friends. Should life-streams joint with multimedia data generated by passive life experience capturing systems, the traffic related with these life-stream applications will become huge. In parallel, the proliferation of sensors and the rise of the Internet of things are expected to generate also a large amount of data streams. Both life-streams and sensor-generated streams require time-shifted navigation. Here, these services reveal another critical issue of time-shifted streaming systems: privacy protection. Sensitive life-streams or personal sensor-generated streams highlight the ethical limitations of any architecture having a potential point of control: lesser privacy protection, data lock-in or third-party control. We need a fully distributed system guarantying that the whole stream is actually available, including the most unpopular past portions, and that any past portion can be fetch.

Preserving the privacy of users, and lowering the infrastructure cost for innovative newcomers. Here are the two main motivations for decentralized peer-to-peer systems. It is also a topic that I want to explore further, following two recent papers: here and here. Anybody onboard?