Mind the Gap: Modelling Video Delivery Under Expected Periods of Disconnection

1. Mind the Gap:Modelling Video Delivery Under Expected Periods of Disconnection Argyrios G. Tasiopoulos, IoannisPsaras, and George Pavlou Department of Electronic & Electrical Engineering University College London ACM CHANTS 2014Maui-Hawaii

2. Outline • Introduction • Motivation • Aims • Scope • Model • Evaluation • Conclusions & Future work ACM CHANTS 2014Maui-Hawaii

3. Motivation • In big cities (London, New York, Tokyo, etc.) public transport is the preferred mean of travelling • Almost all commuters are equipped with smartphones • Live streaming is an increasingly popular smartphone application INTRODUCTION ACM CHANTS 2014Maui-Hawaii

4. A Usual Situation INTRODUCTION ACM CHANTS 2014Maui-Hawaii

5. Aims • Questions that we want to answer: • Could we quantify commuters’ Quality of Experience (QoE) of video streaming? • Could we find the benefit of cooperative video streaming in terms of QoE in this setting? • Yes!!! If we could create a model able to calculate over time for each case the: • Playback time • Playback disruptions time INTRODUCTION ACM CHANTS 2014Maui-Hawaii

6. Scope • We focus on the “Tube” environment • Expected Intermittent Connectivity • Internet connectivity only available in train stations • We focus on “Wi-Fi” Internet connectivity • Offered usually for “free” • Trend of installing “Wi-Fi” hotspots INTRODUCTION ACM CHANTS 2014Maui-Hawaii

7. Outline • Introduction • Model • Fundamentals • Utility Functions • Evaluation • Conclusions & Future work ACM CHANTS 2014Maui-Hawaii

8. Fundamentals • Expected Intermittent Connectivity: • We know the time that a train spends in a station  Connection Period: for station i • We also know the time needed to reach the next one  Disconnection Period: • An epoch i, ,consists of a connection and disconnection period MODEL ACM CHANTS 2014Maui-Hawaii

9. Intermittent Connectivity Time i i+1 t=1 MODEL ACM CHANTS 2014Maui-Hawaii

10. Fundamentals (2/4) • Limited bandwidth of Wi-Fi AP • Limited by Wi-Fi AP and network infrastructure • We assume that is shared equally among users • Video/Content consists of chunks • Collection of video packets • Specific bit-rate • Specific playback duration • Two video streaming approaches, a basic and a cooperative one MODEL ACM CHANTS 2014Maui-Hawaii

11. Fundamentals (3/4) • For the basic video streaming approach we use the “Pull” characterization • Since users retrieve a video individually by “pulling” it chunk by chunk • The chunks received over an epoch i are: • The time is discrete since protocols need some time to reallocate the bandwidth MODEL ACM CHANTS 2014Maui-Hawaii

12. Fundamentals (4/4) • Next we name the cooperative video streaming approach as “PUSH” • Since the commuters have to “Pull” some chunks and then they have to “Share” them with the rest of their group, of magnitude at moment • Thus, the number of chunks received over an epoch i for this approach are: MODEL ACM CHANTS 2014Maui-Hawaii

13. Utility Functions • Playback time until epoch i: • Playback disruption time until epoch i: • “Pull” utility function: • Where is the delay sensitivity coefficient • “Push”utility function: • Where is the energy spent by a user for broadcasting his/her content in terms of playback time that could be downloaded from a WiFi AP MODEL ACM CHANTS 2014Maui-Hawaii

14. Outline • Introduction • Model • Evaluation • Theoretical Setting • Realistic Setting • Conclusions & Future work ACM CHANTS 2014Maui-Hawaii

15. Theoretical Setting • In this setting: • All epochs have the same overall duration • The disconnection to duration ratio, , is constant for all epochs: • The number of users remain stable over all epochs • The following results produced for: • 100 commuters, shared bandwidth 54 Mbps, chunk bit-rate 419 Kbps and playback duration 5’’, delay sensitivity 1, and a connection period of 30’’ EVALUATION ACM CHANTS 2014Maui-Hawaii

16. Theoretical Results Pull Case EVALUATION ACM CHANTS 2014Maui-Hawaii

17. Theoretical Results PUSH Case • Group size: 10 commuters EVALUATION ACM CHANTS 2014Maui-Hawaii

18. Pull-PUSH Comparison M=1 EVALUATION ACM CHANTS 2014Maui-Hawaii

19. Realistic Setting • In this setting: • Here we use real traces of London Underground • We focus on Victoria Line which has the least intersections • But still… we have to find a way in order to form groups of users interested in the same content EVALUATION ACM CHANTS 2014Maui-Hawaii

20. Content Assignment Algorithm • Each user who does not watch a content in the beginning of an epoch creates a content with probability • Else he/she joins an existed content, created during this epoch, according to Zipf’s distribution with Zipf’s exponent • Please note that the in these content there is always included the “empty” one EVALUATION ACM CHANTS 2014Maui-Hawaii

21. Realistic Set.: PUSH results over time EVALUATION ACM CHANTS 2014Maui-Hawaii

22. Realistic Set.: Pull results over time EVALUATION ACM CHANTS 2014Maui-Hawaii

23. Realistic Set.: PUSH results over epochs for various “p_new” EVALUATION ACM CHANTS 2014Maui-Hawaii

24. Outline • Introduction • Model • Evaluation • Conclusions & Future work ACM CHANTS 2014Maui-Hawaii

25. Conclusions and Future Work • Conclusions: • We quantified the QoE of video streaming for the “Tube” setting • We found the gains offered by the collaboration among users in case of “few” available contents • Future work: • Include the cellular case • Provide incentives for users to form a groups • Maybe we will crease the content diversity CONCLUSIONS & FUTURE WORK ACM CHANTS 2014Maui-Hawaii

26. Thank You