1. Management of Broadband Media Assets on Wide Area Networks Lars-Olof Burchard

2. 2 Motivation: TV Anytime Services  Web Services: anything, anywhere, anytime  Vision: Access to broadband media content delivered by TV channels at any time  Limiting factor: Performance of networks will never be enough to support interactive access to masses TV content (in MPEG quality)  Approach: Install (caching, mirror) server systems close to the user Advantage: Fast and cheap access for local users Disadvantage: large storage costs for audio/video content  Idea of TV Anytime: Store large quantities of digital TV content on local servers  Content distribution using classical networks: satellite, cable

3. 3 Implementation of TV Anytime  Options for server implementation of TV Anytime services: Local server, e.g. within the living room of a user Distributed server system: public server installed within ADSL, CATV central office and direct line from server to local user (not congested)  Advantages local server Cheap access to stored content items (media assets) Easy content management (access management, security, billing, user data,... )  Advantages distributed servers Popular content items can be shared by many user (minimisation of overall storage) Large servers allow the provision of more content  Our solution: Hierarchy of small local servers (home servers) and larger networked servers

4. 4 Hierarchical VoD servers for TV Anytime services TV Cable Network PC LAN Broadband Network

5. 5 Issues for hierarchical TV Anytime server networks  Technical issues building up coherent network of CATV, ADSL, ATM,... lines  User management, access management, legal issues,....  Decision for recording / deletion of media asset: Decisions about recording / deletion have to be made Build virtual large server that collects all clients that are connected to the sub-tree  Studied here: Decision of placement of media asset on server hierarchy (using different qualities, i.e. bit rates)  Trade-off in placement of media assets onto hierarchical server network between available bandwidth/storage space and bit rate  Solution: Simulated annealing algorithm to solve combinatorial optimisation problem

6. 6 Solution strategy for combinatorial optimization problem  Goal: maximisation of QoS (i.e. bit rate of requested assets)  Simulated Annealing as classical (problem independent) solution method  Proven strategy for combinatorial optimisation problems  Static scenario compute assignment of a fixed set of assets (based on known user behaviour; using profiling, subscriptions)  Dynamic scenario: additional assets are recorded, others deleted during run time reduction to static problem

7. 7 Results  Benchmarks: trees with varying number of nodes, various amounts of assets  Benchmark results: comparison to upper bound BIN_7: 4.31 %, BIN_15: 4.80 %, BIN_32: 5.25 %, BIN_64: 7.88 % TER_4: 4.36 %, TER_14: 4.22 %, TER_40: 9.13 % QUAD_5: 4.69 %, QUAD_21: 6.23 %, QUAD_85: 9.44 %  within 4 to 10 % to upper bound of SA heuristic (smaller difference to optimal solution)